Abstract

We present a study of computational through-wall imaging using a dynamically reconfigurable metasurface antenna (DMA). The DMA consists of a single-feed, electrically-large microstrip line, loaded with individually addressable metamaterial radiators. Each metamaterial resonator is integrated with a diode, enabling it to be switched on (radiating) or off (non-radiating) by an externally applied voltage. By switching subsets of the array of elements on or off, spatially diverse radiation patterns are formed that are scattered by the wall and structures beyond the wall. Images can be reconstructed from these measurements, using a combination of range migration algorithms and wall compensation algorithms, with minimal frequency bandwidth requirements; even single frequency measurements are possible in conjunction with the DMA. We investigate imaging through a variety of wall materials at K-band frequencies (18-26.5 GHz), including homogeneous media with known properties and inhomogeneous materials such as plywood. We further investigate single-frequency performance against full-bandwidth measurements. The DMA used here is electrically large in one dimension, over which many spatially diverse measurements can be taken. By scanning the DMA in the perpendicular direction, full two-dimensional scans can be acquired with minimal cost and time, making the one-dimensional DMA attractive as the basis for future through-wall scanning systems.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

The advantage of microwaves in through-wall imaging stems from the ability of microwaves to penetrate optically-opaque materials without the deleterious effects associated with ionizing radiation [13]. However, microwaves are characterized by large wavelengths; thus, achieving desired resolution limits in the range and cross-range dimensions typically requires ultrawide bandwidth combined with large antenna arrays or mechanically scanned antennas [49]. These imaging mechanisms can achieve excellent performance, but generally require costly and often complex hardware, as well as long data acquisition times. When large bandwidths are used to provide range resolution, as is often the case in coherent imaging systems that rely on radar-like techniques, another layer of complexity is added to the system. Specifically, wideband systems necessitate a more elaborate radio frequency (RF) backend that must operate in an already congested microwave spectrum [1012]. Large aperture, wideband, and scanning systems are thus generally restricted to applications with relaxed time and cost constraints.

Many of the difficulties associated with conventional imaging techniques can be overcome using computational imaging, in which software and hardware are often co-designed to improve imaging system complexity, speed, or cost. Prominent examples in the optical regime include single pixel imaging and ptychography, in which high-resolution, high-field-of-view imaging is possible. Applying similar ideas to microwave frequencies has led to computational microwave imaging paradigms in which conventional antennas with well-defined radiation patterns are replaced by larger single-port antennas that can generate sets of diverse multi-lobe patterns with low spatial correlation [1315]. Such patterns (depicted in Fig. 1) can illuminate a scene and multiplex its spatial content into a set of backscatter measurements. A single port, electrically-large antenna can thus create a collection of patterns using either spatial or frequency diversity, effectively replacing an array of antennas of equivalent size.

 

Fig. 1. (a) A schematic of a microstrip-based dynamic metasurface antenna and (b) a close up view of its constituent resonant elements. A via connects the central region of the cELC to the bias line, which lies below the ground plane. The red and green diodes show the binary behavior by which the metamaterial elements can tune their interactions with the guided mode. (c) Employing two antennas allows for the creation of complex electric fields in the scene, as depicted by the multiplication of the Tx and Rx fields shown in blue-green. A wall is also depicted in (c) and can be seen to distort the fields.

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Within the computational microwave imaging framework, waveguide-fed metasurface apertures have shown great potential [16]. A waveguide-fed metasurface aperture consists of an electrically large waveguide, in which the waveguide feed excites a collection of metamaterial irises etched into one of its bounding conductors. Distinct radiation patterns are formed whenever the guided wave [14,17] or the resonator properties are altered [15]. If the quality (Q-) factor of the resonators is high enough and the resonance frequencies of the elements are distributed over the bandwidth of operation, then diverse radiation patterns are formed as a function of the driving frequency and no other modulation is required. However, if the properties of the metamaterial elements are modified dynamically through electronic tuning, then a set of diverse radiation patterns can be formed directly by changing the resonator configurations (or masks) at a particular frequency. When the elements are tuned dynamically, we refer to the antenna as a dynamic metasurface antenna (DMA). In this latter case, the bandwidth requirement can be minimal. If the aperture is large enough that the region of interest lies within the Fresnel zone (or radiating near-field), both cross-range and range information can be determined even at a single frequency. In this sense, the imaging system no longer relies on a wide bandwidth to obtain range resolution, as is often the case in microwave (radar-like) imaging systems. This behavior is achievable because of the oblique look-angles available to the electrically large aperture. A recent work has explored the idea of single frequency imaging using a pair of DMAs (one acting as a transmitter and one as a receiver) [18]. Single frequency imaging was recently extended to operations with intensity-only measurements [19,20].

In the present work we consider a through-wall imaging (TWI) system comprising two 1D DMAs [18], with the designation "1D" indicating that the waveguide extends along one dimension, such as a microstrip or rectangular waveguide. We show that the metasurface aperture can offer significant hardware advantages over traditional TWI approaches (such as synthetic aperture radar (SAR) and multiple-input multiple-output (MIMO)) in terms of the number of antennas/transceivers and bandwidth. We find that single-frequency operation shows enhanced robustness in scenarios where the wall material properties are unknown and inhomogeneous—a feature desired in many applications. Here, we extend the work in [18], which was focused entirely on single-frequency imaging in a homogeneous environment, to imaging through inhomogeneous dielectric layers. In studying the TWI scenario, we perform a thorough comparison of wide bandwidth to single frequency imaging, in which we find that single frequency operation eliminates many complications associated with imaging through layers of unknown properties.

To achieve viable image reconstructions, we adapt processing techniques relevant to TWI to the case of a (wideband) system with electrically-large DMAs. Using these techniques, we are able to account for reflections from the wall and phase distortions caused by the wall when using DMAs. By combining the processing algorithms with the DMA hardware, we propose a TWI approach in which we leverage the multiplexing feature of the DMA to mitigate wall reflection by ensemble averaging over multiple measurements of the scene. To achieve this averaging necessitates an intermediate step in which the fields are propagated through the wall before image reconstruction occurs. After applying these pre-processing techniques, we then reconstruct using a modified form of the range migration algorithm [21].

This paper is organized as follows. Section 2 of this paper summarizes the imaging system developed in [18], including an introduction to the hardware and post-processing techniques. In Section 3, we review the effects of including planar stratified walls in microwave imaging. After showing TWI with walls possessing known properties we explore some more complex walls—where phase compensation is not as accessible—in Section 4. Comparisons between monotone and bandwidth systems are particularly emphasized in Sections 3.24.

2. Imaging using electrically-large DMAs

2.1 Physical platform

In this work, we use a pair of DMAs designed for the lower K band (18 GHz to 20 GHz), one for transmission and the other for reception, as illustrated in Fig. 1. The DMAs are identical and take the form of 1D microstrip waveguides with complimentary electric-LC (cELC) resonators etched into their traces. The resonators, $112$ per each DMA, are spaced at 3.33 mm (${\approx }\,\lambda /4$) and span roughly 40 cm. Each element includes PIN diodes and can be tuned independently by digital circuitry [16]. When a voltage bias is applied to an element, its resonance is eliminated by transitioning it out of the operational bandwidth. This gives a binary amplitude modulation where an element can either be in a radiating (on) or non-radiating (off) state. In the off state an element allows energy to pass unperturbed and the wave excites subsequent elements. When the element is on it couples a portion of the incident energy out of the waveguide as free space radiation. The radiation pattern of the overall aperture is the superposition of the fields radiated by the collection of on elements. Energy is inserted at three locations (at the center and at each end) that are fed simultaneously using a power divider, which takes in an RF signal at a single port. More details on the operation of the DMA considered here can be found in [22].

In this configuration, $N_m$-many sequential measurements with different on/off modulation patterns (referred to as masks) allow for the signals at different locations along the antenna to be measured and decoupled [23,24]. That is to say, with a sufficient number of measurements, the platform can obtain information equivalent to a MIMO system where the different antennas along the MIMO array are sampled separately [18]. The sequential patterns generated by the DMAs can be related to effective dipoles in the aperture plane of the DMAs using surface equivalence principles [21]. Considering the set of effective dipoles as a multiplexing matrix the MIMO analogy becomes clear. As opposed to turning on a single element at a time, resembling a SAR or MIMO configuration, multiplexing the signal across a collection of randomly chosen resonators results in information gathered uniformly across the aperture in parallel.

The patterns generated by the DMAs are characterized experimentally using near-field scanning, in which the emitted tangential electric fields for all of the different mask-frequency combinations are measured [25]. Each of the antennas, positioned side-by-side as shown in Fig. 2, is characterized through this process for all $N_m$ masks. The data for the $N_s$-many near-field scan positions is included in an $N_s {\times } N_m$ matrix $\boldsymbol {\Phi }_{T,f}$ for a given frequency $f$ for the transmitter (and similarly for the receiver, $\boldsymbol {\Phi }_{R,f}$). Since the antennas are 1D, the patterns only exhibit significant variation in the xy plane (as defined in Fig. 1) and we only need to characterize the antenna’s azimuthal patterns. Reconstructions are conducted in the $xy$ plane, corresponding to the plane formed by the range and cross-range dimensions. It is worth emphasizing that these concepts can be extended to a 3D system using a 2D aperture (such as an array of 1D waveguides or a parallel plate waveguide) [26,27] or by scanning in the perpendicular direction—this opportunity is left to future work.

 

Fig. 2. The imaging system with two dynamic metasurface apertures, a polystyrene wall, and objects arranged in a 2D scene.

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The data acquisition process is completed with a single radio transceiver that feeds the transmitting DMA on Port $1$ and samples the signal of the receiving DMA on Port $2$. During a given measurement, each DMA cycles through $N_m=100$ masks and the radio sweeps from 17.86 GHz to 19.93 GHz in steps of 0.27 GHz (totaling $N_f=8$ frequency samples). To achieve desired performance, only $30$ randomly-chosen elements are allowed to radiate at any given time. The choices of $N_m=100$ and $30$ simultaneous radiators are based on empirical investigations in [22] and [18], and are utilized to ensure that the entire aperture is filled. All permutations of the Tx and Rx masks are used (thus $100{\times }100{\times }8$ total measurements). Later we will take subsets of this data to compare the cases of single-frequency imaging and bandwidth imaging. While this data structure can be likened to a data cube for a $100{\times }100$ MIMO system, the spatial dimensions are replaced by “mask indexing" dimensions which must be processed to relate the $N_m {\times } N_m$ SISO measurements to the $N_s {\times } N_s$ MIMO equivalent.

2.2 Image reconstruction process

Once the antennas are characterized and backscatter measurements are collected, an image of the region of interest (ROI) is computationally reconstructed. The data are processed with the adapted range migration algorithm (RMA) [2830] developed in [18] and [31] with modifications to account for multiple frequencies. We complete the $k$-space modeling for each frequency independently and then sum across the bandwidth. A final inverse finite Fourier transform (IFFT) step then creates a reconstruction of the scene. We let $\mathbf {G}_f$ be the matrix of measurements ($N_m{\times }N_m$) for a single frequency. This matrix can be converted to its equivalent MIMO version, where each location in the equivalent array is measured separately, by taking into account the antenna fields $\boldsymbol {\Phi }_{T/R,f}$. This conversion is established through

$$\mathbf{s}_{0,f} = (\boldsymbol{\Phi}^+_{T,f})^T \mathbf{G}_f \boldsymbol{\Phi}^+_{R,f}$$
where $+$ is the pseudo-inverse operation and $T$ is the transpose operation [18,23]. The pseudo-inverse is calculated via the truncated singular value decomposition (SVD) method with a normalized threshold of $0.05$ (obtained empirically). The resultant matrix $\mathbf {s}_{0,f}$ has dimensions of $N_s{\times }N_s$ (corresponding to the number of positions along each antenna) and represents the equivalent MIMO signals between each position on the transmitter and receiver. The truncation threshold reflects how linearly independent the radiated patterns are. Too much truncation will create gaps in the aperture, leading to aliasing, while too little truncation will amplify noise. Note that the threshold is the same for all reconstructions presented to maintain fair comparisons. While we set the threshold in this paper empirically, it is worth noting that the thresholding level may also be selected intelligently as a function of the SNR (which is dependent on the scene and frequency of operation) [32,33]. An investigation of intelligent threshold selection (especially when dealing with different frequencies) is left for future work.

Once the signal has been represented in this manner, we take the Fourier transform across the spatial dimensions of the transmitter and receiver, account for the offsets of the antenna/scene locations, and perform an interpolation (more steps are given in Eqs. 7–11 in [18] but are omitted here for brevity). This leaves us with $S_{I,f}$, the interpolated data in $k$-space on a per-frequency basis. For this work, we then sum over all of the frequencies in the operational bandwidth $B$ and complete the final IFFT of the RMA as

$$\hat{\sigma}(x,y) = {\bigg|} \mathcal{F}^{{-}1}_{2D} {\bigg(} \sum_{f \in B} S_{I,f}(k_x, k_y) {\bigg)} {\bigg|}.$$
The resulting $\hat {\sigma }$ is the estimation of the scene’s reflectivity. Note that this processing scheme is developed without regard to the inclusion of a wall. Further modifications to compensate for the wall’s behavior will be introduced in Section 3.2.

3. Adaptations of the DMA Configuration for Through-Wall Imaging

Figure 2 shows the DMAs, the wall, and the scene. As described above, the DMAs are positioned to create a 1D system. They are placed perpendicular to the wall’s normal at approximately $x=15$ cm. The emitted fields are polarized with the magnetic field only having a $z$ component, making the fields transverse magnetic ($\textrm {TM}_{\textrm {z}}$) in the plane of interest.

The DMAs can be conveniently consider as being composed of effective sources—in this case magnetic currents or, when discretized, magnetic dipoles—that are calculated from the near-field scan [34]. The in-plane radiation from these z-polarized dipoles can be described as cylindrical waves that encounter the wall and are deformed. The deformation of the waves caused by the refraction and phase retardation experienced as they transit the wall leads to distortion of the sampled $k$-space points. Many existing works are dedicated to efficiently modeling the fields in this setting [3538]; here, we take a relatively simple ray-optics approach, since we are focusing on the computational imaging concepts instead of rigorous formulations for propagating through inhomogeneous media. As we will see in later sections, many of these processing techniques become unnecessary when operating at a single frequency.

In addition to the complexities introduced by the transmission through layered media, the primary reflection from the wall has a debilitating effect on the image formation. Our goals for adapting the DMA-based imaging system for TWI are thus twofold:

  • 1. Account for the signal backscattered from the wall in order to isolate signals from the ROI, and
  • 2. Compensate for the phase retardation and refraction associated with the propagation through the wall.

These two tasks will be described and validated in the next two subsections.

3.1 Accounting For wall reflections

Before presenting methods to account for the wall reflections, we first experimentally demonstrate the negative effects that the wall has on the image quality. For this subsection we will make use of the full dataset with the entire frequency range ($100{\times }100{\times }8$ measurements) and exclusively use a polystyrene wall ($\epsilon =2.53$) that is 5 cm thick.

In Fig. 3 we show images of a scene, including two cylindrical metallic objects, and reconstructions with and without the wall. The images are reconstructed with the method discussed in Sec. 2.2 and are shown from an aerial (top-down) perspective. Figure 3b shows the case when there is no wall, and the image in Fig. 3c shows the image of the same target in the presence of the wall. An empty scene in the presence of the wall is also shown in Fig. 3d. Comparing these figures, it can be seen that that the wall is the source of the artifacts encountered in Figs. 3c, d.

 

Fig. 3. Imaging results of a pair of cylindrical objects, shown in (a), for the cases when there (b) no wall, (c) a wall but no clutter mitigation, and (d) the wall alone.

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Our first task is to account for the wall reflections so that the signal from the ROI is not overwhelmed by the signal from the wall. This problem is often referred to as clutter mitigation [5,9,39,40]. We will show two methods to account for these reflections: the first is a minor modification of standard background subtraction and the second, which is more practical, subtracts the wall contribution through multiple measurements.

Standard background subtraction takes two measurements, one with the antennas in an isolated environment (with the wall and no objects) and one with objects present, and subtracts the former from the latter. The result of this background subtraction method is shown in Fig. 4a. Although this process is common in the literature, it is unfortunately not easy to execute in practice since we usually do not have access to the wall alone. However, it can serve as a ground truth to assess more practical clutter mitigation as discussed next.

 

Fig. 4. Imaging results of a pair of scatterers after completing clutter mitigation with (a) method 1, traditional background subtraction, and (b) method 2, ensemble averaging.

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As an alternative, we can take an average over several scenes (with different objects in the scene or by moving the DMAs across the wall) and use this average to process the data. This approach is the moving average approach or the spatial filtering approach which is sometimes employed in SAR and array antennas [39,41]. The notion is that the wall is the one component of the return signal that remains constant as the antenna position is changed, while the reflections from the objects average to zero [39]. Using DMAs with their spatially distinct patterns in fact perfectly matches this process; the averaging or multiplexing of many looks associated with the DMA allows us to move our system by a smaller distance compared to conventional systems. Note that in the current experimental setup, it is easier to move the scene or make small changes to the object’s locations, so we will use this strategy. The result with this ensemble backgrounding approach is shown in Fig. 4b and it can be seen that this method performs as well as the background subtraction. In the result in Fig. 4b, we have completed this process with $25$ different scene realizations. It should also be noted that the scene depicted in Fig. 4b is part of the ensemble data set. This means there will be a minor hit in the image’s dynamic range since we have subtracted a portion of the scene signal which corresponds to the ROI we have imaged. This effect can be mitigated with more realizations, and increasing the number of different scenes will only make the image better.

3.2 Compensating for wall distortion

The images in Fig. 4 successfully account for wall reflections, but it should be noticed that the objects have experienced a shift in the range direction. Additionally, the sidelobes are significantly larger (Fig. 4) than the original image with no wall (Fig. 3b). To compensate for these effects, caused by the refraction and phase delay that occur when the waves propagate through the wall, a more substantial modification must be completed. Our aim is to do so while maintaining the reconstruction method in Section 2.2 since it is relatively efficient compared to iterative approaches.

Compensating for the wall’s effects has been a point of focus in the TWI literature. Some efforts have modeled the wall as a single slab with an angularly-dependent transmission coefficient [5] and others have tried to compensate for all of the multiple reflections, not only within the wall but also between the wall and the scene [38,42,43]. Many radar-type works follow a framework that takes into consideration the direct path through the wall. For a single antenna and a single point in the scene, the angle of refraction and the ray path can be calculated if the wall’s properties are known [44,45]. This method has been used to find the fields at the points in the scene [9] and sometimes to find far-field patterns for beamformers [44].

Instead of calculating the signal at all points in the scene (or for the entire far-field) from a single antenna, we will build a set of virtual sources on the far side of the wall which act as a virtual DMA. This notion and all of the relevant parameters are depicted in Fig. 5. Once this virtual DMA is created we can process the data with the same RMA reconstruction that was discussed in Sec. 2.2 and demonstrated in Sec. 3.1. It should also be noted that computing these effective sources (and any analytic model to account for propagation through the wall) requires an estimate of the wall’s characteristics, such as permittivity and thickness, and typically assumes a homogeneous media. In future works, one can use autofocusing algorithms to jointly estimate the wall response and the image [46].

 

Fig. 5. The geometry and parameters of the planar stratified TWI problem, used to calculate effective sources $\boldsymbol {\Psi }$ from the original sources $\boldsymbol {\Phi }$.

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The wall is offset from the original sources ($\boldsymbol {\Phi }_{T/R,f}$) by a distance of $d_1$, the wall has thickness $d_2$, and the new set of sources will be at a distance of $d_3$ from the wall. These $N_v$-many virtual sources will be denoted as $\boldsymbol {\Psi }_{T/R,f}$ ($N_v{\times }N_m$) and will be related to the original sources through a matrix $\boldsymbol {\Gamma }_{T/R,f}$ as

$$\boldsymbol{\Psi}_{T/R,f} = \boldsymbol{\Gamma}_{T/R,f} \boldsymbol{\Phi}_{T/R,f}.$$
The matrix $\boldsymbol {\Gamma }_{T/R,f}$ is $N_v{\times }N_s$ and relates each of the source locations from the near-field scan to the virtual sources. Since the system is linear shift invariant, $\boldsymbol {\Gamma }_{T/R,f}$ will be a Toeplitz matrix and only a single row (or column) must be calculated (with some overfill). Physically speaking, this description means that the offset $\Delta y$ (between the original source and the virtual source) is the only variable that the transfer function depends on. We can thus calculate the transmission compensation $\gamma$, a single entry in $\boldsymbol {\Gamma }_{T/R,f}$, as
$$\gamma(\Delta y,f) \propto E_\perp (\Delta y, f)$$
where $E_\perp$ is the tangential electric field from the original source at the virtual source’s location. If desired, the layered Green’s function could be used to calculate $E_\perp$ and therefore $\boldsymbol {\Gamma }_{T/R,f}$. As mentioned above, we will follow the method in [44] which relies on a ray tracing approximation and ignores multiple reflections in the wall.

Calculating $E_\perp$ with the ray tracing approximation here means calculating the plane wave with its wavevector along that ray. We will also include the energy lost upon the initial scattering events associated with the interfaces via transmission coefficients—$T_{12}$ when entering the wall and $T_{23}=2-T_{12}$ when exiting (because the first and last media are both free space). For this case

$$T_{12} = \frac{2 \epsilon_{\textrm{wall}} k_{0x}}{\epsilon_{\textrm{wall}} k_{0x} + k_{\textrm{wall},x}}$$
where $k_{0x}$ and $k_{\textrm {wall},x}$ are the $x$-projections of the $k$-vectors in the respective media [47]. The tangential electric field is then
$$E_\perp{=} \cos(\theta) T_{12} T_{23} e^{{-}j(k_0p_1 + k_{\textrm{wall}}p_2 + k_0p_3)}$$
where $p_1$, $p_2$, and $p_3$ are the lengths of the ray segments and $\theta$ is the angle of the ray with respect to the antenna’s broadside (the cosine term exists here because we only need the tangential component to compute the field everywhere beyond the wall according to the surface equivalence principle). Note that knowledge of $\theta$ is required throughout the formulation, but that it can be found fairly simply from the geometry of the problem [44].

Formulating the problem in this manner is convenient since $\boldsymbol {\Gamma }_{T/R,f}$ only needs to be calculated once, as opposed to propagating each radiation pattern through the wall independently. This aspect becomes especially necessary if a more complex approach is taken, such as the layered Green’s function, since only Eq. (4) must be computed for a range of $\Delta y$. Of course, much of this formulation is also contingent on a flat wall that is correctly aligned to the antenna, but some generalization may be made for minor misalignments if necessary. It is also worth mentioning that the method outlined here is unique to the setup used here and and other systems that use electrically large antennas. For systems involving small dipolar antennas, this step may not be necessary.

3.3 Experimental demonstration of wall compensation for DMA-based imaging system

We now put these techniques to use and demonstrate their validity using scene consisting of six metallic cylinders (each with a radius of 1.5 cm). We reconstruct this scene using $10\%$ fractional bandwidth as well as using only single-frequency measurements—referred to here as the bandwidth and monotone cases, respectively. For the bandwidth case we use all of the frequency data taken (17.86 GHz to 19.93 GHz with eight evenly-spaced samples) but only use $36{\times }36$ masks, totaling $10{,}368$ measurements. For the monotone case we use only 19.03 GHz and complete $100{\times }100$ masks for a total of $10{,}000$ measurements. The number of measurements is balanced to ensure fairness from a signal-to-noise ratio (SNR) perspective.

Figure 6 shows TWI results for the bandwidth and monotone cases in the top and bottom rows, respectively. Results are included for the cases of no wall; a wall but with no compensation; and a wall with compensation included. The ensemble averaging method was utilized for background subtraction in the through-wall images (Figs. 6b,c and 6e,f). Some interesting conclusions can be drawn from these results. Looking only at the bandwidth case it is evident that the wall severely degrades the image quality when no compensation is applied. Figure 6c shows that the wall compensation is successful and restores the image to be nearly the same quality as the no-wall case.

 

Fig. 6. Imaging results for the bandwidth case (a-c) and monotone case (d-f). The images in (a) and (d) show the results without any wall; (b) and (e) show the results after clutter mitigation only; and (c) and (f) show the results after both clutter mitigation and wall compensation. The objects are shown schematically in (g).

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Another interesting result is observed when looking at the monotone case. The lack of compensation does not have a significant impact on the imaging performance. In fact, Figs. 6d-f are fairly consistent in terms of image quality. This enhanced robustness (as compared to the bandwidth case) can be better understood by considering the effect of the wall on the received signal at a single frequency and over a bandwidth. For the single-frequency case, the presence of the wall introduces a phase shift across all measurements. Since there is only one frequency of operation, the shift is consistent for all of the measurements across tuning states. In the bandwidth case, the distortion of the return signal is different for each frequency; i.e. the measurements experience a frequency dependent phase shift. In essence, this means that multiple data points with disagreeing information are mapped to the same point. This in turn leads to interfering information and gives a severely degraded result.

The deteriorated result described above will be exacerbated as larger bandwidths are used, meaning that it is especially important for ultra-wideband systems to take the wall’s effects into consideration. Although Fig. 6c shows that completing the compensation process removes this conflicting data, such compensation may not always be so simple. Nonplanar walls, inhomogeneous materials, uncharacterized dielectric properties, and unknown wall thicknesses can all complicate the compensation process. The fact that the single-frequency data does not experience significant distortions without compensation means that it can be utilized without knowledge of the wall’s characteristics. Neither the wall’s location nor the wall’s material properties are needed to complete the imaging process with high fidelity (as evidenced by Fig. 6e).

The use of a more complicated object in Fig. 6 shows how severely the image can be degraded from a qualitative perspective. For a more quantitative comparison, we perform a point spread function (PSF) analysis. Here the PSF is given by the reconstruction of a point-like object, i.e. a cylinder with radius less than the resolution limit. The PSF reconstruction is completed for monotone and bandwidth cases, both with and without wall compensation. The results are shown in Fig. 7, with cross sections also taken along the main axes. In the cases without compensation it is seen that the object has shifted its position—this is consistent with expectations and has been confirmed in other works [4,5]. For the monotone cases, the image is nearly identical with and without compensation, but the shift still exists when wall compensation is not utilized. When bandwidth is used, it is again seen that the image is degraded and artifacts begin to appear. (Note that we have increased the range of the colormap in Figs. 7a-d to see more subtle effects.) Once compensation is included, the image appears at the same location—the true location—for both the bandwidth and monotone cases.

 

Fig. 7. Point spread functions for the various cases are shown in (a-d), and cross sections through the objects location are shown in (e) and (f) for cross-range and range, respectively. (a) shows bandwidth with no compensation, (b) shows bandwidth with compensation, (c) shows monotone with no compensation, and (d) shows bandwidth with compensation.

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It is worth noting that the range and cross-range resolution of the system can be estimated experimentally from the result in Fig. 6. Comparing these results to the analytic diffraction limit is more easily completed in a free space setting as was done in [18]. The resolution is spatially-variant—i.e. it depends on the location in the scene—and therefore plotting the PSF at a single point does not give a complete assessment of the system. More details can be found in [18] where a comparison across the entire scene was completed and it was found that the resolution matches with expectation within a factor of $2$. In the present work the presence of the wall has a minimal impact on the resolution, as can be seen by comparing the result of 6(a) and 6(c).

Examining the results of Figs. 67, we can also see other interesting opportunities. For example, a comparison between wall auto-focusing methods for the cases of the single frequency result and the wideband results would be interesting [46]. Also, one could use the single frequency result as a priori knowledge for a bandwidth case or one could combine the methods to characterize the wall (or another dielectric of interest) in combination with the imaging procedure.

4. Single-frequency imaging

Motivated by the single-frequency imaging results observed in the previous section, we now compare the monotone versus the bandwidth cases in the context of more complex TWI scenarios. Looking closely at Fig. 7, it is worth pointing out that the range resolution is the same for both the monotone and bandwidth cases after compensation has been completed. This indicates that the bandwidth plays a negligible role in resolving along the range direction and that the electrically-large size of the aperture in combination with the operation in the radiative near-field is the primary contributor. This result reinforces the notion that a single-frequency imaging system can perform high-quality imaging (regardless of a the wall’s presence) while remaining more cost effective and robust than ultra-wideband systems.

To explore this comparison of robustness more explicitly in the TWI environment we will image with more complicated walls. Specifically, we will look at a $2$-cm-thick wall made of plywood and a wall made of $1.5$-cm-thick fiberboard with two coats of acrylic paint. The grain size of the medium density fiberboard (MDF) is rather small compared to the wavelength and the wall can therefore be considered as homogeneous. The plywood is slightly rougher in composition and has more variation (especially along the range direction). Furthermore, the plywood is warped and is not entirely parallel to the antennas. Neither one of the materials has been characterized nor are we making any attempt at estimating the wall’s properties. These considerations make autofocusing algorithms and methods of wall compensation particularly difficult, if not untenable.

The reconstructions for these two walls are shown in Fig. 8 for the bandwidth and monotone cases. Two separate scenes are shown: for the MDF case (a,b) there are four objects, for the plywood case (c,d) there are five objects. In both cases it appears that the monotone case outperforms the bandwidth case. The plywood case is particularly difficult for both the monotone case and the bandwidth case, likely due to higher attenuation in plywood. Nonetheless, we can conclude that using DMAs over a single frequency enables an imaging system that is simple and capable to image through walls with unknown properties, in contrast to previous works which involve autofocusing and optimization algorithms [5,4850] or time consuming measurements at different distances/polarizations [45,51,52].

 

Fig. 8. Imaging results for walls made of medium density fiberboard (a-b) and plywood (c-d). Bandwidth results are shown in (a) and (c), and monotone results are shown in (b) and (d). Objects for (a-b) and (c-d) are shown schematically in (e) and (f), respectively. Clutter mitigation is used in all cases, but no compensation for propagation through the wall is employed.

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In addition to performance, single-frequency imaging is of interest because of numerous other considerations. Particularly, the narrowband radio hardware that would support a single-frequency system (VCOs, filters, power dividers, etc.) is less expensive and will have better performance. Susceptibility to external interference, which is increasingly problematic in the congested microwave spectrum, may also be reduced for the single-frequency case. Allocation of spectrum will also be less restricting if a technology is developed for a single frequency as opposed to consuming an ultra wideband.

5. Conclusion

In this manuscript, we have employed a pair of dynamic metasurface antennas in a through-wall imaging system. We have implemented techniques to mitigate wall clutter and to compensate for the effects of transmission through the wall. Specifically, for wall clutter mitigation we showed the effectiveness of the background subtraction technique and employed an ensemble averaging approach which takes advantage of the DMAs’ spatially-diverse radiation. For through-wall compensation, we created a virtual aperture on the far side of the wall that only required a transfer function through the wall, after which reconstruction could be completed without consideration for the wall.

A key contribution of this work is the development of a single-frequency TWI system. We compared the use of a monotone signal to a wideband signal and showed how bandwidth impacted imaging performance. The compensation technique was effective for both cases, but it was seen that the single-frequency case could operate satisfactorily without wall compensation. Additionally, range-wise imaging was possible with the single-frequency case owing to the fact that we operate in close proximity to an electrically-large antenna. No deterioration in cross-range or range resolution was observed between the monotone and bandwidth cases.

The electrically-large size and rapid reconfigurability of the 1D DMAs means that the current platform can be advantageous compared with many SAR modalities. In particular, scanning a simple antenna over a full 2D aperture can be excessively time-consuming, especially given the precision needed for coherent measurements. By contrast, the 1D DMA achieves imaging within a plane through the use of an ensemble of diverse radiation patterns generated dynamically. These patterns can be generated rapidly using the PIN diode tuning described here—in milliseconds or less. Combined with a mechanical scan along the perpendicular direction, the hardware costs and complexity of the DMA system could be quite minimal. The DMA system may also be favorable over MIMO arrays, because a single transceiver is required and the RF layers are less hardware-intensive. Finally, single-frequency operation demonstrated has the potential to be cost-effective as well as robust, in addition to a host of other benefits [18].

Funding

Air Force Office of Scientific Research (FA9550-12-1-0491, FA9550-18-1-0187).

Acknowledgments

The authors also thank D. Arnitz and M. Reynolds who were responsible for the development of the custom FMCW radio prior to its repurposed implementation in the present work.

Disclosures

The authors declare no conflicts of interest.

References

1. D. M. Sheen, D. L. McMakin, and T. E. Hall, “Three-dimensional millimeter-wave imaging for concealed weapon detection,” IEEE Trans. Microwave Theory Tech. 49(9), 1581–1592 (2001). [CrossRef]  

2. E. J. Bond, X. Li, S. C. Hagness, and B. D. Van Veen, “Microwave imaging via space-time beamforming for early detection of breast cancer,” IEEE Trans. Antennas Propag. 51(8), 1690–1705 (2003). [CrossRef]  

3. R. P. Dooley, “X-band holography,” Proc. IEEE 53(11), 1733–1735 (1965). [CrossRef]  

4. T. Jin and A. Yarovoy, “A through-the-wall radar imaging method based on a realistic model,” Int. J. Antennas Propag. 2015, 1–8 (2015). [CrossRef]  

5. M. Dehmollaian and K. Sarabandi, “Refocusing through building walls using synthetic aperture radar,” IEEE Trans. Geosci. Remote. Sens. 46(6), 1589–1599 (2008). [CrossRef]  

6. Y. S. Yoon and M. G. Amin, “High-resolution through-the-wall radar imaging using beamspace music,” IEEE Trans. Antennas Propag. 56(6), 1763–1774 (2008). [CrossRef]  

7. Q. Huang, L. Qu, B. Wu, and G. Fang, “Uwb through-wall imaging based on compressive sensing,” IEEE Trans. Geosci. Remote. Sens. 48(3), 1408–1415 (2010). [CrossRef]  

8. J. Peabody Jr, G. L. Charvat, J. Goodwin, and M. Tobias, “Through-wall imaging radar, Tech. rep.,” Massachusetts Institute of Technology-Lincoln Laboratory Lexington United States (2012).

9. W. Zhang and A. Hoorfar, “Three-dimensional synthetic aperture radar imaging through multilayered walls,” IEEE Trans. Antennas Propag. 62(1), 459–462 (2014). [CrossRef]  

10. M. Soumekh, “A system model and inversion for synthetic aperture radar imaging,” IEEE Trans. on Image Process. 1(1), 64–76 (1992). [CrossRef]  

11. M. Soumekh, Fourier Array Imaging (Prentice-Hall, Inc., 1994).

12. M. Soumekh, Synthetic Aperture Radar Signal Processing (John Wiley & Sons, 1999).

13. J. Hunt, J. Gollub, T. Driscoll, G. Lipworth, A. Mrozack, M. S. Reynolds, D. J. Brady, and D. R. Smith, “Metamaterial microwave holographic imaging system,” J. Opt. Soc. Am. A 31(10), 2109–2119 (2014). [CrossRef]  

14. J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017). [CrossRef]  

15. T. Sleasman, M. F. Imani, J. N. Gollub, and D. R. Smith, “Dynamic metamaterial aperture for microwave imaging,” Appl. Phys. Lett. 107(20), 204104 (2015). [CrossRef]  

16. T. Sleasman, M. F. Imani, W. Xu, J. Hunt, T. Driscoll, M. S. Reynolds, and D. R. Smith, “Waveguide-fed tunable metamaterial element for dynamic apertures,” IEEE Antennas Wirel. Propag. Lett. 15, 606–609 (2016). [CrossRef]  

17. T. Sleasman, M. F. Imani, J. N. Gollub, and D. R. Smith, “Microwave imaging using a disordered cavity with a dynamically tunable impedance surface,” Phys. Rev. Appl. 6(5), 054019 (2016). [CrossRef]  

18. T. Sleasman, M. Boyarsky, M. F. Imani, T. Fromenteze, J. N. Gollub, and D. R. Smith, “Single-frequency microwave imaging with dynamic metasurface apertures,” J. Opt. Soc. Am. B 34(8), 1713–1726 (2017). [CrossRef]  

19. A. V. Diebold, M. F. Imani, T. Sleasman, and D. R. Smith, “Phaseless computational ghost imaging at microwave frequencies using a dynamic metasurface aperture,” Appl. Opt. 57(9), 2142–2149 (2018). [CrossRef]  

20. A. V. Diebold, M. F. Imani, T. Sleasman, and D. R. Smith, “Phaseless coherent and incoherent microwave ghost imaging with dynamic metasurface apertures,” Optica 5(12), 1529–1541 (2018). [CrossRef]  

21. A. V. Diebold, L. Pulido-Mancera, T. Sleasman, M. Boyarsky, M. F. Imani, and D. R. Smith, “Generalized range migration algorithm for synthetic aperture radar image reconstruction of metasurface antenna measurements,” J. Opt. Soc. Am. B 34(12), 2610–2623 (2017). [CrossRef]  

22. T. Sleasman, M. Boyarsky, M. F. Imani, J. Gollub, and D. Smith, “Design considerations for a dynamic metamaterial aperture for computational imaging at microwave frequencies,” J. Opt. Soc. Am. B 33(6), 1098–1111 (2016). [CrossRef]  

23. T. Fromenteze, E. L. Kpre, D. Carsenat, C. Decroze, and T. Sakamoto, “Single-shot compressive multiple-inputs multiple-outputs radar imaging using a two-port passive device,” IEEE Access 4, 1050–1060 (2016). [CrossRef]  

24. L. Pulido-Mancera, T. Fromenteze, T. Sleasman, M. Boyarsky, M. F. Imani, M. Reynolds, and D. R. Smith, “Application of range migration algorithms to imaging with a dynamic metasurface antenna,” J. Opt. Soc. Am. B 33(10), 2082–2092 (2016). [CrossRef]  

25. A. D. Yaghjian, “An overview of near-field antenna measurements,” IEEE Trans. Antennas Propag. 34(1), 30–45 (1986). [CrossRef]  

26. M. Boyarsky, T. Sleasman, L. Pulido-Mancera, A. V. Diebold, M. F. Imani, and D. R. Smith, “Single-frequency 3d synthetic aperture imaging with dynamic metasurface antennas,” Appl. Opt. 57(15), 4123–4134 (2018). [CrossRef]  

27. M. F. Imani, T. Sleasman, and D. R. Smith, “Two-dimensional dynamic metasurface apertures for computational microwave imaging,” IEEE Antennas Wirel. Propag. Lett. 17(12), 2299–2303 (2018). [CrossRef]  

28. R. Solimene, I. Catapano, G. Gennarelli, A. Cuccaro, A. Dell’Aversano, and F. Soldovieri, “Sar imaging algorithms and some unconventional applications: A unified mathematical overview,” IEEE Signal Process. Mag. 31(4), 90–98 (2014). [CrossRef]  

29. J. M. Lopez-Sanchez and J. Fortuny-Guasch, “3-d radar imaging using range migration techniques,” IEEE Trans. Antennas Propag. 48(5), 728–737 (2000). [CrossRef]  

30. R. Bamler, “A comparison of range-doppler and wavenumber domain sar focusing algorithms,” IEEE Trans. Geosci. Remote. Sens. 30(4), 706–713 (1992). [CrossRef]  

31. T. Fromenteze, M. Boyarsky, J. Gollub, T. Sleasman, M. F. Imani, and D. R. Smith, “Single-frequency near-field mimo imaging,” in 2017 11th European Conference on Antennas and Propagation (EUCAP), (IEEE, 2017), pp. 1415–1418.

32. P. C. Hansen, “Analysis of discrete ill-posed problems by means of the l-curve,” SIAM Rev. 34(4), 561–580 (1992). [CrossRef]  

33. P. C. Hansen and D. P. O’Leary, “The use of the l-curve in the regularization of discrete ill-posed problems,” SIAM J. on Sci. Comput. 14(6), 1487–1503 (1993). [CrossRef]  

34. G. Lipworth, A. Rose, O. Yurduseven, V. R. Gowda, M. F. Imani, H. Odabasi, P. Trofatter, J. Gollub, and D. R. Smith, “Comprehensive simulation platform for a metamaterial imaging system,” Appl. Opt. 54(31), 9343–9353 (2015). [CrossRef]  

35. K. A. Michalski and J. R. Mosig, “Multilayered media green’s functions in integral equation formulations,” IEEE Trans. Antennas Propag. 45(3), 508–519 (1997). [CrossRef]  

36. W. C. Chew, Waves and fields in Inhomogeneous Media (IEEE, 1995).

37. T. J. Cui and W. C. Chew, “Fast evaluation of sommerfeld integrals for em scattering and radiation by three-dimensional buried objects,” IEEE Trans. Geosci. Remote. Sens. 37(2), 887–900 (1999). [CrossRef]  

38. M. Leigsnering, F. Ahmad, M. Amin, and A. Zoubir, “Multipath exploitation in through-the-wall radar imaging using sparse reconstruction,” IEEE Trans. Aerosp. Electron. Syst. 50(2), 920–939 (2014). [CrossRef]  

39. Y. S. Yoon and M. G. Amin, “Spatial filtering for wall-clutter mitigation in through-the-wall radar imaging,” IEEE Trans. Geosci. Remote. Sens. 47(9), 3192–3208 (2009). [CrossRef]  

40. M. Dehmollaian, M. Thiel, and K. Sarabandi, “Through-the-wall imaging using differential sar,” IEEE Trans. Geosci. Remote. Sens. 47(5), 1289–1296 (2009). [CrossRef]  

41. H. Brunzell, “Detection of shallowly buried objects using impulse radar,” IEEE Trans. Geosci. Remote. Sens. 37(2), 875–886 (1999). [CrossRef]  

42. T. M. Habashy, R. W. Groom, and B. R. Spies, “Beyond the born and rytov approximations: A nonlinear approach to electromagnetic scattering,” J. Geophys. Res.: Solid Earth 98(B2), 1759–1775 (1993). [CrossRef]  

43. L.-P. Song, C. Yu, and Q. H. Liu, “Through-wall imaging (twi) by radar: 2-d tomographic results and analyses,” IEEE Trans. Geosci. Remote. Sens. 43(12), 2793–2798 (2005). [CrossRef]  

44. F. Ahmad, M. G. Amin, and S. A. Kassam, “Synthetic aperture beamformer for imaging through a dielectric wall,” IEEE Trans. Aerosp. Electron. Syst. 41(1), 271–283 (2005). [CrossRef]  

45. C. Thajudeen and A. Hoorfar, “A comparative study of wall parameter estimation and mitigation techniques,” in 2010 USNC/URSI Meeting, (2010).

46. L. Li, W. Zhang, and F. Li, “A novel autofocusing approach for real-time through-wall imaging under unknown wall characteristics,” IEEE Trans. Geosci. Remote. Sens. 48(1), 423–431 (2010). [CrossRef]  

47. C. A. Balanis, Advanced Engineering Eectromagnetics (John Wiley & Sons, 1999).

48. F. Ahmad, M. G. Amin, and G. Mandapati, “Autofocusing of through-the-wall radar imagery under unknown wall characteristics,” IEEE Trans. on Image Process. 16(7), 1785–1795 (2007). [CrossRef]  

49. V. H. Tang, A. Bouzerdoum, and S. L. Phung, “Multipolarization through-wall radar imaging using low-rank and jointly-sparse representations,” IEEE Trans. on Image Process. 27(4), 1763–1776 (2018). [CrossRef]  

50. D. L. Marks, O. Yurduseven, and D. R. Smith, “Sparse blind deconvolution for imaging through layered media,” Optica 4(12), 1514–1521 (2017). [CrossRef]  

51. G. Wang and M. G. Amin, “Imaging through unknown walls using different standoff distances,” IEEE Trans. Signal Process. 54(10), 4015–4025 (2006). [CrossRef]  

52. K. M. Yemelyanov, N. Engheta, A. Hoorfar, and J. A. McVay, “Adaptive polarization contrast techniques for through-wall microwave imaging applications,” IEEE Trans. Geosci. Remote. Sens. 47(5), 1362–1374 (2009). [CrossRef]  

References

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  1. D. M. Sheen, D. L. McMakin, and T. E. Hall, “Three-dimensional millimeter-wave imaging for concealed weapon detection,” IEEE Trans. Microwave Theory Tech. 49(9), 1581–1592 (2001).
    [Crossref]
  2. E. J. Bond, X. Li, S. C. Hagness, and B. D. Van Veen, “Microwave imaging via space-time beamforming for early detection of breast cancer,” IEEE Trans. Antennas Propag. 51(8), 1690–1705 (2003).
    [Crossref]
  3. R. P. Dooley, “X-band holography,” Proc. IEEE 53(11), 1733–1735 (1965).
    [Crossref]
  4. T. Jin and A. Yarovoy, “A through-the-wall radar imaging method based on a realistic model,” Int. J. Antennas Propag. 2015, 1–8 (2015).
    [Crossref]
  5. M. Dehmollaian and K. Sarabandi, “Refocusing through building walls using synthetic aperture radar,” IEEE Trans. Geosci. Remote. Sens. 46(6), 1589–1599 (2008).
    [Crossref]
  6. Y. S. Yoon and M. G. Amin, “High-resolution through-the-wall radar imaging using beamspace music,” IEEE Trans. Antennas Propag. 56(6), 1763–1774 (2008).
    [Crossref]
  7. Q. Huang, L. Qu, B. Wu, and G. Fang, “Uwb through-wall imaging based on compressive sensing,” IEEE Trans. Geosci. Remote. Sens. 48(3), 1408–1415 (2010).
    [Crossref]
  8. J. Peabody, G. L. Charvat, J. Goodwin, and M. Tobias, “Through-wall imaging radar, Tech. rep.,” Massachusetts Institute of Technology-Lincoln Laboratory Lexington United States (2012).
  9. W. Zhang and A. Hoorfar, “Three-dimensional synthetic aperture radar imaging through multilayered walls,” IEEE Trans. Antennas Propag. 62(1), 459–462 (2014).
    [Crossref]
  10. M. Soumekh, “A system model and inversion for synthetic aperture radar imaging,” IEEE Trans. on Image Process. 1(1), 64–76 (1992).
    [Crossref]
  11. M. Soumekh, Fourier Array Imaging (Prentice-Hall, Inc., 1994).
  12. M. Soumekh, Synthetic Aperture Radar Signal Processing (John Wiley & Sons, 1999).
  13. J. Hunt, J. Gollub, T. Driscoll, G. Lipworth, A. Mrozack, M. S. Reynolds, D. J. Brady, and D. R. Smith, “Metamaterial microwave holographic imaging system,” J. Opt. Soc. Am. A 31(10), 2109–2119 (2014).
    [Crossref]
  14. J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017).
    [Crossref]
  15. T. Sleasman, M. F. Imani, J. N. Gollub, and D. R. Smith, “Dynamic metamaterial aperture for microwave imaging,” Appl. Phys. Lett. 107(20), 204104 (2015).
    [Crossref]
  16. T. Sleasman, M. F. Imani, W. Xu, J. Hunt, T. Driscoll, M. S. Reynolds, and D. R. Smith, “Waveguide-fed tunable metamaterial element for dynamic apertures,” IEEE Antennas Wirel. Propag. Lett. 15, 606–609 (2016).
    [Crossref]
  17. T. Sleasman, M. F. Imani, J. N. Gollub, and D. R. Smith, “Microwave imaging using a disordered cavity with a dynamically tunable impedance surface,” Phys. Rev. Appl. 6(5), 054019 (2016).
    [Crossref]
  18. T. Sleasman, M. Boyarsky, M. F. Imani, T. Fromenteze, J. N. Gollub, and D. R. Smith, “Single-frequency microwave imaging with dynamic metasurface apertures,” J. Opt. Soc. Am. B 34(8), 1713–1726 (2017).
    [Crossref]
  19. A. V. Diebold, M. F. Imani, T. Sleasman, and D. R. Smith, “Phaseless computational ghost imaging at microwave frequencies using a dynamic metasurface aperture,” Appl. Opt. 57(9), 2142–2149 (2018).
    [Crossref]
  20. A. V. Diebold, M. F. Imani, T. Sleasman, and D. R. Smith, “Phaseless coherent and incoherent microwave ghost imaging with dynamic metasurface apertures,” Optica 5(12), 1529–1541 (2018).
    [Crossref]
  21. A. V. Diebold, L. Pulido-Mancera, T. Sleasman, M. Boyarsky, M. F. Imani, and D. R. Smith, “Generalized range migration algorithm for synthetic aperture radar image reconstruction of metasurface antenna measurements,” J. Opt. Soc. Am. B 34(12), 2610–2623 (2017).
    [Crossref]
  22. T. Sleasman, M. Boyarsky, M. F. Imani, J. Gollub, and D. Smith, “Design considerations for a dynamic metamaterial aperture for computational imaging at microwave frequencies,” J. Opt. Soc. Am. B 33(6), 1098–1111 (2016).
    [Crossref]
  23. T. Fromenteze, E. L. Kpre, D. Carsenat, C. Decroze, and T. Sakamoto, “Single-shot compressive multiple-inputs multiple-outputs radar imaging using a two-port passive device,” IEEE Access 4, 1050–1060 (2016).
    [Crossref]
  24. L. Pulido-Mancera, T. Fromenteze, T. Sleasman, M. Boyarsky, M. F. Imani, M. Reynolds, and D. R. Smith, “Application of range migration algorithms to imaging with a dynamic metasurface antenna,” J. Opt. Soc. Am. B 33(10), 2082–2092 (2016).
    [Crossref]
  25. A. D. Yaghjian, “An overview of near-field antenna measurements,” IEEE Trans. Antennas Propag. 34(1), 30–45 (1986).
    [Crossref]
  26. M. Boyarsky, T. Sleasman, L. Pulido-Mancera, A. V. Diebold, M. F. Imani, and D. R. Smith, “Single-frequency 3d synthetic aperture imaging with dynamic metasurface antennas,” Appl. Opt. 57(15), 4123–4134 (2018).
    [Crossref]
  27. M. F. Imani, T. Sleasman, and D. R. Smith, “Two-dimensional dynamic metasurface apertures for computational microwave imaging,” IEEE Antennas Wirel. Propag. Lett. 17(12), 2299–2303 (2018).
    [Crossref]
  28. R. Solimene, I. Catapano, G. Gennarelli, A. Cuccaro, A. Dell’Aversano, and F. Soldovieri, “Sar imaging algorithms and some unconventional applications: A unified mathematical overview,” IEEE Signal Process. Mag. 31(4), 90–98 (2014).
    [Crossref]
  29. J. M. Lopez-Sanchez and J. Fortuny-Guasch, “3-d radar imaging using range migration techniques,” IEEE Trans. Antennas Propag. 48(5), 728–737 (2000).
    [Crossref]
  30. R. Bamler, “A comparison of range-doppler and wavenumber domain sar focusing algorithms,” IEEE Trans. Geosci. Remote. Sens. 30(4), 706–713 (1992).
    [Crossref]
  31. T. Fromenteze, M. Boyarsky, J. Gollub, T. Sleasman, M. F. Imani, and D. R. Smith, “Single-frequency near-field mimo imaging,” in 2017 11th European Conference on Antennas and Propagation (EUCAP), (IEEE, 2017), pp. 1415–1418.
  32. P. C. Hansen, “Analysis of discrete ill-posed problems by means of the l-curve,” SIAM Rev. 34(4), 561–580 (1992).
    [Crossref]
  33. P. C. Hansen and D. P. O’Leary, “The use of the l-curve in the regularization of discrete ill-posed problems,” SIAM J. on Sci. Comput. 14(6), 1487–1503 (1993).
    [Crossref]
  34. G. Lipworth, A. Rose, O. Yurduseven, V. R. Gowda, M. F. Imani, H. Odabasi, P. Trofatter, J. Gollub, and D. R. Smith, “Comprehensive simulation platform for a metamaterial imaging system,” Appl. Opt. 54(31), 9343–9353 (2015).
    [Crossref]
  35. K. A. Michalski and J. R. Mosig, “Multilayered media green’s functions in integral equation formulations,” IEEE Trans. Antennas Propag. 45(3), 508–519 (1997).
    [Crossref]
  36. W. C. Chew, Waves and fields in Inhomogeneous Media (IEEE, 1995).
  37. T. J. Cui and W. C. Chew, “Fast evaluation of sommerfeld integrals for em scattering and radiation by three-dimensional buried objects,” IEEE Trans. Geosci. Remote. Sens. 37(2), 887–900 (1999).
    [Crossref]
  38. M. Leigsnering, F. Ahmad, M. Amin, and A. Zoubir, “Multipath exploitation in through-the-wall radar imaging using sparse reconstruction,” IEEE Trans. Aerosp. Electron. Syst. 50(2), 920–939 (2014).
    [Crossref]
  39. Y. S. Yoon and M. G. Amin, “Spatial filtering for wall-clutter mitigation in through-the-wall radar imaging,” IEEE Trans. Geosci. Remote. Sens. 47(9), 3192–3208 (2009).
    [Crossref]
  40. M. Dehmollaian, M. Thiel, and K. Sarabandi, “Through-the-wall imaging using differential sar,” IEEE Trans. Geosci. Remote. Sens. 47(5), 1289–1296 (2009).
    [Crossref]
  41. H. Brunzell, “Detection of shallowly buried objects using impulse radar,” IEEE Trans. Geosci. Remote. Sens. 37(2), 875–886 (1999).
    [Crossref]
  42. T. M. Habashy, R. W. Groom, and B. R. Spies, “Beyond the born and rytov approximations: A nonlinear approach to electromagnetic scattering,” J. Geophys. Res.: Solid Earth 98(B2), 1759–1775 (1993).
    [Crossref]
  43. L.-P. Song, C. Yu, and Q. H. Liu, “Through-wall imaging (twi) by radar: 2-d tomographic results and analyses,” IEEE Trans. Geosci. Remote. Sens. 43(12), 2793–2798 (2005).
    [Crossref]
  44. F. Ahmad, M. G. Amin, and S. A. Kassam, “Synthetic aperture beamformer for imaging through a dielectric wall,” IEEE Trans. Aerosp. Electron. Syst. 41(1), 271–283 (2005).
    [Crossref]
  45. C. Thajudeen and A. Hoorfar, “A comparative study of wall parameter estimation and mitigation techniques,” in 2010 USNC/URSI Meeting, (2010).
  46. L. Li, W. Zhang, and F. Li, “A novel autofocusing approach for real-time through-wall imaging under unknown wall characteristics,” IEEE Trans. Geosci. Remote. Sens. 48(1), 423–431 (2010).
    [Crossref]
  47. C. A. Balanis, Advanced Engineering Eectromagnetics (John Wiley & Sons, 1999).
  48. F. Ahmad, M. G. Amin, and G. Mandapati, “Autofocusing of through-the-wall radar imagery under unknown wall characteristics,” IEEE Trans. on Image Process. 16(7), 1785–1795 (2007).
    [Crossref]
  49. V. H. Tang, A. Bouzerdoum, and S. L. Phung, “Multipolarization through-wall radar imaging using low-rank and jointly-sparse representations,” IEEE Trans. on Image Process. 27(4), 1763–1776 (2018).
    [Crossref]
  50. D. L. Marks, O. Yurduseven, and D. R. Smith, “Sparse blind deconvolution for imaging through layered media,” Optica 4(12), 1514–1521 (2017).
    [Crossref]
  51. G. Wang and M. G. Amin, “Imaging through unknown walls using different standoff distances,” IEEE Trans. Signal Process. 54(10), 4015–4025 (2006).
    [Crossref]
  52. K. M. Yemelyanov, N. Engheta, A. Hoorfar, and J. A. McVay, “Adaptive polarization contrast techniques for through-wall microwave imaging applications,” IEEE Trans. Geosci. Remote. Sens. 47(5), 1362–1374 (2009).
    [Crossref]

2018 (5)

2017 (4)

2016 (5)

T. Sleasman, M. F. Imani, J. N. Gollub, and D. R. Smith, “Microwave imaging using a disordered cavity with a dynamically tunable impedance surface,” Phys. Rev. Appl. 6(5), 054019 (2016).
[Crossref]

L. Pulido-Mancera, T. Fromenteze, T. Sleasman, M. Boyarsky, M. F. Imani, M. Reynolds, and D. R. Smith, “Application of range migration algorithms to imaging with a dynamic metasurface antenna,” J. Opt. Soc. Am. B 33(10), 2082–2092 (2016).
[Crossref]

T. Sleasman, M. F. Imani, W. Xu, J. Hunt, T. Driscoll, M. S. Reynolds, and D. R. Smith, “Waveguide-fed tunable metamaterial element for dynamic apertures,” IEEE Antennas Wirel. Propag. Lett. 15, 606–609 (2016).
[Crossref]

T. Fromenteze, E. L. Kpre, D. Carsenat, C. Decroze, and T. Sakamoto, “Single-shot compressive multiple-inputs multiple-outputs radar imaging using a two-port passive device,” IEEE Access 4, 1050–1060 (2016).
[Crossref]

T. Sleasman, M. Boyarsky, M. F. Imani, J. Gollub, and D. Smith, “Design considerations for a dynamic metamaterial aperture for computational imaging at microwave frequencies,” J. Opt. Soc. Am. B 33(6), 1098–1111 (2016).
[Crossref]

2015 (3)

T. Sleasman, M. F. Imani, J. N. Gollub, and D. R. Smith, “Dynamic metamaterial aperture for microwave imaging,” Appl. Phys. Lett. 107(20), 204104 (2015).
[Crossref]

T. Jin and A. Yarovoy, “A through-the-wall radar imaging method based on a realistic model,” Int. J. Antennas Propag. 2015, 1–8 (2015).
[Crossref]

G. Lipworth, A. Rose, O. Yurduseven, V. R. Gowda, M. F. Imani, H. Odabasi, P. Trofatter, J. Gollub, and D. R. Smith, “Comprehensive simulation platform for a metamaterial imaging system,” Appl. Opt. 54(31), 9343–9353 (2015).
[Crossref]

2014 (4)

W. Zhang and A. Hoorfar, “Three-dimensional synthetic aperture radar imaging through multilayered walls,” IEEE Trans. Antennas Propag. 62(1), 459–462 (2014).
[Crossref]

J. Hunt, J. Gollub, T. Driscoll, G. Lipworth, A. Mrozack, M. S. Reynolds, D. J. Brady, and D. R. Smith, “Metamaterial microwave holographic imaging system,” J. Opt. Soc. Am. A 31(10), 2109–2119 (2014).
[Crossref]

R. Solimene, I. Catapano, G. Gennarelli, A. Cuccaro, A. Dell’Aversano, and F. Soldovieri, “Sar imaging algorithms and some unconventional applications: A unified mathematical overview,” IEEE Signal Process. Mag. 31(4), 90–98 (2014).
[Crossref]

M. Leigsnering, F. Ahmad, M. Amin, and A. Zoubir, “Multipath exploitation in through-the-wall radar imaging using sparse reconstruction,” IEEE Trans. Aerosp. Electron. Syst. 50(2), 920–939 (2014).
[Crossref]

2010 (2)

L. Li, W. Zhang, and F. Li, “A novel autofocusing approach for real-time through-wall imaging under unknown wall characteristics,” IEEE Trans. Geosci. Remote. Sens. 48(1), 423–431 (2010).
[Crossref]

Q. Huang, L. Qu, B. Wu, and G. Fang, “Uwb through-wall imaging based on compressive sensing,” IEEE Trans. Geosci. Remote. Sens. 48(3), 1408–1415 (2010).
[Crossref]

2009 (3)

Y. S. Yoon and M. G. Amin, “Spatial filtering for wall-clutter mitigation in through-the-wall radar imaging,” IEEE Trans. Geosci. Remote. Sens. 47(9), 3192–3208 (2009).
[Crossref]

M. Dehmollaian, M. Thiel, and K. Sarabandi, “Through-the-wall imaging using differential sar,” IEEE Trans. Geosci. Remote. Sens. 47(5), 1289–1296 (2009).
[Crossref]

K. M. Yemelyanov, N. Engheta, A. Hoorfar, and J. A. McVay, “Adaptive polarization contrast techniques for through-wall microwave imaging applications,” IEEE Trans. Geosci. Remote. Sens. 47(5), 1362–1374 (2009).
[Crossref]

2008 (2)

Y. S. Yoon and M. G. Amin, “High-resolution through-the-wall radar imaging using beamspace music,” IEEE Trans. Antennas Propag. 56(6), 1763–1774 (2008).
[Crossref]

M. Dehmollaian and K. Sarabandi, “Refocusing through building walls using synthetic aperture radar,” IEEE Trans. Geosci. Remote. Sens. 46(6), 1589–1599 (2008).
[Crossref]

2007 (1)

F. Ahmad, M. G. Amin, and G. Mandapati, “Autofocusing of through-the-wall radar imagery under unknown wall characteristics,” IEEE Trans. on Image Process. 16(7), 1785–1795 (2007).
[Crossref]

2006 (1)

G. Wang and M. G. Amin, “Imaging through unknown walls using different standoff distances,” IEEE Trans. Signal Process. 54(10), 4015–4025 (2006).
[Crossref]

2005 (2)

L.-P. Song, C. Yu, and Q. H. Liu, “Through-wall imaging (twi) by radar: 2-d tomographic results and analyses,” IEEE Trans. Geosci. Remote. Sens. 43(12), 2793–2798 (2005).
[Crossref]

F. Ahmad, M. G. Amin, and S. A. Kassam, “Synthetic aperture beamformer for imaging through a dielectric wall,” IEEE Trans. Aerosp. Electron. Syst. 41(1), 271–283 (2005).
[Crossref]

2003 (1)

E. J. Bond, X. Li, S. C. Hagness, and B. D. Van Veen, “Microwave imaging via space-time beamforming for early detection of breast cancer,” IEEE Trans. Antennas Propag. 51(8), 1690–1705 (2003).
[Crossref]

2001 (1)

D. M. Sheen, D. L. McMakin, and T. E. Hall, “Three-dimensional millimeter-wave imaging for concealed weapon detection,” IEEE Trans. Microwave Theory Tech. 49(9), 1581–1592 (2001).
[Crossref]

2000 (1)

J. M. Lopez-Sanchez and J. Fortuny-Guasch, “3-d radar imaging using range migration techniques,” IEEE Trans. Antennas Propag. 48(5), 728–737 (2000).
[Crossref]

1999 (2)

T. J. Cui and W. C. Chew, “Fast evaluation of sommerfeld integrals for em scattering and radiation by three-dimensional buried objects,” IEEE Trans. Geosci. Remote. Sens. 37(2), 887–900 (1999).
[Crossref]

H. Brunzell, “Detection of shallowly buried objects using impulse radar,” IEEE Trans. Geosci. Remote. Sens. 37(2), 875–886 (1999).
[Crossref]

1997 (1)

K. A. Michalski and J. R. Mosig, “Multilayered media green’s functions in integral equation formulations,” IEEE Trans. Antennas Propag. 45(3), 508–519 (1997).
[Crossref]

1993 (2)

T. M. Habashy, R. W. Groom, and B. R. Spies, “Beyond the born and rytov approximations: A nonlinear approach to electromagnetic scattering,” J. Geophys. Res.: Solid Earth 98(B2), 1759–1775 (1993).
[Crossref]

P. C. Hansen and D. P. O’Leary, “The use of the l-curve in the regularization of discrete ill-posed problems,” SIAM J. on Sci. Comput. 14(6), 1487–1503 (1993).
[Crossref]

1992 (3)

P. C. Hansen, “Analysis of discrete ill-posed problems by means of the l-curve,” SIAM Rev. 34(4), 561–580 (1992).
[Crossref]

R. Bamler, “A comparison of range-doppler and wavenumber domain sar focusing algorithms,” IEEE Trans. Geosci. Remote. Sens. 30(4), 706–713 (1992).
[Crossref]

M. Soumekh, “A system model and inversion for synthetic aperture radar imaging,” IEEE Trans. on Image Process. 1(1), 64–76 (1992).
[Crossref]

1986 (1)

A. D. Yaghjian, “An overview of near-field antenna measurements,” IEEE Trans. Antennas Propag. 34(1), 30–45 (1986).
[Crossref]

1965 (1)

R. P. Dooley, “X-band holography,” Proc. IEEE 53(11), 1733–1735 (1965).
[Crossref]

Ahmad, F.

M. Leigsnering, F. Ahmad, M. Amin, and A. Zoubir, “Multipath exploitation in through-the-wall radar imaging using sparse reconstruction,” IEEE Trans. Aerosp. Electron. Syst. 50(2), 920–939 (2014).
[Crossref]

F. Ahmad, M. G. Amin, and G. Mandapati, “Autofocusing of through-the-wall radar imagery under unknown wall characteristics,” IEEE Trans. on Image Process. 16(7), 1785–1795 (2007).
[Crossref]

F. Ahmad, M. G. Amin, and S. A. Kassam, “Synthetic aperture beamformer for imaging through a dielectric wall,” IEEE Trans. Aerosp. Electron. Syst. 41(1), 271–283 (2005).
[Crossref]

Amin, M.

M. Leigsnering, F. Ahmad, M. Amin, and A. Zoubir, “Multipath exploitation in through-the-wall radar imaging using sparse reconstruction,” IEEE Trans. Aerosp. Electron. Syst. 50(2), 920–939 (2014).
[Crossref]

Amin, M. G.

Y. S. Yoon and M. G. Amin, “Spatial filtering for wall-clutter mitigation in through-the-wall radar imaging,” IEEE Trans. Geosci. Remote. Sens. 47(9), 3192–3208 (2009).
[Crossref]

Y. S. Yoon and M. G. Amin, “High-resolution through-the-wall radar imaging using beamspace music,” IEEE Trans. Antennas Propag. 56(6), 1763–1774 (2008).
[Crossref]

F. Ahmad, M. G. Amin, and G. Mandapati, “Autofocusing of through-the-wall radar imagery under unknown wall characteristics,” IEEE Trans. on Image Process. 16(7), 1785–1795 (2007).
[Crossref]

G. Wang and M. G. Amin, “Imaging through unknown walls using different standoff distances,” IEEE Trans. Signal Process. 54(10), 4015–4025 (2006).
[Crossref]

F. Ahmad, M. G. Amin, and S. A. Kassam, “Synthetic aperture beamformer for imaging through a dielectric wall,” IEEE Trans. Aerosp. Electron. Syst. 41(1), 271–283 (2005).
[Crossref]

Arnitz, D.

J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017).
[Crossref]

Balanis, C. A.

C. A. Balanis, Advanced Engineering Eectromagnetics (John Wiley & Sons, 1999).

Bamler, R.

R. Bamler, “A comparison of range-doppler and wavenumber domain sar focusing algorithms,” IEEE Trans. Geosci. Remote. Sens. 30(4), 706–713 (1992).
[Crossref]

Bond, E. J.

E. J. Bond, X. Li, S. C. Hagness, and B. D. Van Veen, “Microwave imaging via space-time beamforming for early detection of breast cancer,” IEEE Trans. Antennas Propag. 51(8), 1690–1705 (2003).
[Crossref]

Bouzerdoum, A.

V. H. Tang, A. Bouzerdoum, and S. L. Phung, “Multipolarization through-wall radar imaging using low-rank and jointly-sparse representations,” IEEE Trans. on Image Process. 27(4), 1763–1776 (2018).
[Crossref]

Boyarsky, M.

M. Boyarsky, T. Sleasman, L. Pulido-Mancera, A. V. Diebold, M. F. Imani, and D. R. Smith, “Single-frequency 3d synthetic aperture imaging with dynamic metasurface antennas,” Appl. Opt. 57(15), 4123–4134 (2018).
[Crossref]

A. V. Diebold, L. Pulido-Mancera, T. Sleasman, M. Boyarsky, M. F. Imani, and D. R. Smith, “Generalized range migration algorithm for synthetic aperture radar image reconstruction of metasurface antenna measurements,” J. Opt. Soc. Am. B 34(12), 2610–2623 (2017).
[Crossref]

J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017).
[Crossref]

T. Sleasman, M. Boyarsky, M. F. Imani, T. Fromenteze, J. N. Gollub, and D. R. Smith, “Single-frequency microwave imaging with dynamic metasurface apertures,” J. Opt. Soc. Am. B 34(8), 1713–1726 (2017).
[Crossref]

L. Pulido-Mancera, T. Fromenteze, T. Sleasman, M. Boyarsky, M. F. Imani, M. Reynolds, and D. R. Smith, “Application of range migration algorithms to imaging with a dynamic metasurface antenna,” J. Opt. Soc. Am. B 33(10), 2082–2092 (2016).
[Crossref]

T. Sleasman, M. Boyarsky, M. F. Imani, J. Gollub, and D. Smith, “Design considerations for a dynamic metamaterial aperture for computational imaging at microwave frequencies,” J. Opt. Soc. Am. B 33(6), 1098–1111 (2016).
[Crossref]

T. Fromenteze, M. Boyarsky, J. Gollub, T. Sleasman, M. F. Imani, and D. R. Smith, “Single-frequency near-field mimo imaging,” in 2017 11th European Conference on Antennas and Propagation (EUCAP), (IEEE, 2017), pp. 1415–1418.

Brady, D.

J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017).
[Crossref]

Brady, D. J.

Brunzell, H.

H. Brunzell, “Detection of shallowly buried objects using impulse radar,” IEEE Trans. Geosci. Remote. Sens. 37(2), 875–886 (1999).
[Crossref]

Carsenat, D.

T. Fromenteze, E. L. Kpre, D. Carsenat, C. Decroze, and T. Sakamoto, “Single-shot compressive multiple-inputs multiple-outputs radar imaging using a two-port passive device,” IEEE Access 4, 1050–1060 (2016).
[Crossref]

Catapano, I.

R. Solimene, I. Catapano, G. Gennarelli, A. Cuccaro, A. Dell’Aversano, and F. Soldovieri, “Sar imaging algorithms and some unconventional applications: A unified mathematical overview,” IEEE Signal Process. Mag. 31(4), 90–98 (2014).
[Crossref]

Charvat, G. L.

J. Peabody, G. L. Charvat, J. Goodwin, and M. Tobias, “Through-wall imaging radar, Tech. rep.,” Massachusetts Institute of Technology-Lincoln Laboratory Lexington United States (2012).

Chew, W. C.

T. J. Cui and W. C. Chew, “Fast evaluation of sommerfeld integrals for em scattering and radiation by three-dimensional buried objects,” IEEE Trans. Geosci. Remote. Sens. 37(2), 887–900 (1999).
[Crossref]

W. C. Chew, Waves and fields in Inhomogeneous Media (IEEE, 1995).

Cuccaro, A.

R. Solimene, I. Catapano, G. Gennarelli, A. Cuccaro, A. Dell’Aversano, and F. Soldovieri, “Sar imaging algorithms and some unconventional applications: A unified mathematical overview,” IEEE Signal Process. Mag. 31(4), 90–98 (2014).
[Crossref]

Cui, T. J.

T. J. Cui and W. C. Chew, “Fast evaluation of sommerfeld integrals for em scattering and radiation by three-dimensional buried objects,” IEEE Trans. Geosci. Remote. Sens. 37(2), 887–900 (1999).
[Crossref]

Decroze, C.

T. Fromenteze, E. L. Kpre, D. Carsenat, C. Decroze, and T. Sakamoto, “Single-shot compressive multiple-inputs multiple-outputs radar imaging using a two-port passive device,” IEEE Access 4, 1050–1060 (2016).
[Crossref]

Dehmollaian, M.

M. Dehmollaian, M. Thiel, and K. Sarabandi, “Through-the-wall imaging using differential sar,” IEEE Trans. Geosci. Remote. Sens. 47(5), 1289–1296 (2009).
[Crossref]

M. Dehmollaian and K. Sarabandi, “Refocusing through building walls using synthetic aperture radar,” IEEE Trans. Geosci. Remote. Sens. 46(6), 1589–1599 (2008).
[Crossref]

Dell’Aversano, A.

R. Solimene, I. Catapano, G. Gennarelli, A. Cuccaro, A. Dell’Aversano, and F. Soldovieri, “Sar imaging algorithms and some unconventional applications: A unified mathematical overview,” IEEE Signal Process. Mag. 31(4), 90–98 (2014).
[Crossref]

Diebold, A. V.

Dooley, R. P.

R. P. Dooley, “X-band holography,” Proc. IEEE 53(11), 1733–1735 (1965).
[Crossref]

Driscoll, T.

T. Sleasman, M. F. Imani, W. Xu, J. Hunt, T. Driscoll, M. S. Reynolds, and D. R. Smith, “Waveguide-fed tunable metamaterial element for dynamic apertures,” IEEE Antennas Wirel. Propag. Lett. 15, 606–609 (2016).
[Crossref]

J. Hunt, J. Gollub, T. Driscoll, G. Lipworth, A. Mrozack, M. S. Reynolds, D. J. Brady, and D. R. Smith, “Metamaterial microwave holographic imaging system,” J. Opt. Soc. Am. A 31(10), 2109–2119 (2014).
[Crossref]

Engheta, N.

K. M. Yemelyanov, N. Engheta, A. Hoorfar, and J. A. McVay, “Adaptive polarization contrast techniques for through-wall microwave imaging applications,” IEEE Trans. Geosci. Remote. Sens. 47(5), 1362–1374 (2009).
[Crossref]

Fang, G.

Q. Huang, L. Qu, B. Wu, and G. Fang, “Uwb through-wall imaging based on compressive sensing,” IEEE Trans. Geosci. Remote. Sens. 48(3), 1408–1415 (2010).
[Crossref]

Fortuny-Guasch, J.

J. M. Lopez-Sanchez and J. Fortuny-Guasch, “3-d radar imaging using range migration techniques,” IEEE Trans. Antennas Propag. 48(5), 728–737 (2000).
[Crossref]

Fromenteze, T.

T. Sleasman, M. Boyarsky, M. F. Imani, T. Fromenteze, J. N. Gollub, and D. R. Smith, “Single-frequency microwave imaging with dynamic metasurface apertures,” J. Opt. Soc. Am. B 34(8), 1713–1726 (2017).
[Crossref]

T. Fromenteze, E. L. Kpre, D. Carsenat, C. Decroze, and T. Sakamoto, “Single-shot compressive multiple-inputs multiple-outputs radar imaging using a two-port passive device,” IEEE Access 4, 1050–1060 (2016).
[Crossref]

L. Pulido-Mancera, T. Fromenteze, T. Sleasman, M. Boyarsky, M. F. Imani, M. Reynolds, and D. R. Smith, “Application of range migration algorithms to imaging with a dynamic metasurface antenna,” J. Opt. Soc. Am. B 33(10), 2082–2092 (2016).
[Crossref]

T. Fromenteze, M. Boyarsky, J. Gollub, T. Sleasman, M. F. Imani, and D. R. Smith, “Single-frequency near-field mimo imaging,” in 2017 11th European Conference on Antennas and Propagation (EUCAP), (IEEE, 2017), pp. 1415–1418.

Gennarelli, G.

R. Solimene, I. Catapano, G. Gennarelli, A. Cuccaro, A. Dell’Aversano, and F. Soldovieri, “Sar imaging algorithms and some unconventional applications: A unified mathematical overview,” IEEE Signal Process. Mag. 31(4), 90–98 (2014).
[Crossref]

Gollub, J.

Gollub, J. N.

T. Sleasman, M. Boyarsky, M. F. Imani, T. Fromenteze, J. N. Gollub, and D. R. Smith, “Single-frequency microwave imaging with dynamic metasurface apertures,” J. Opt. Soc. Am. B 34(8), 1713–1726 (2017).
[Crossref]

J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017).
[Crossref]

T. Sleasman, M. F. Imani, J. N. Gollub, and D. R. Smith, “Microwave imaging using a disordered cavity with a dynamically tunable impedance surface,” Phys. Rev. Appl. 6(5), 054019 (2016).
[Crossref]

T. Sleasman, M. F. Imani, J. N. Gollub, and D. R. Smith, “Dynamic metamaterial aperture for microwave imaging,” Appl. Phys. Lett. 107(20), 204104 (2015).
[Crossref]

Goodwin, J.

J. Peabody, G. L. Charvat, J. Goodwin, and M. Tobias, “Through-wall imaging radar, Tech. rep.,” Massachusetts Institute of Technology-Lincoln Laboratory Lexington United States (2012).

Gowda, V. R.

Groom, R. W.

T. M. Habashy, R. W. Groom, and B. R. Spies, “Beyond the born and rytov approximations: A nonlinear approach to electromagnetic scattering,” J. Geophys. Res.: Solid Earth 98(B2), 1759–1775 (1993).
[Crossref]

Habashy, T. M.

T. M. Habashy, R. W. Groom, and B. R. Spies, “Beyond the born and rytov approximations: A nonlinear approach to electromagnetic scattering,” J. Geophys. Res.: Solid Earth 98(B2), 1759–1775 (1993).
[Crossref]

Hagness, S. C.

E. J. Bond, X. Li, S. C. Hagness, and B. D. Van Veen, “Microwave imaging via space-time beamforming for early detection of breast cancer,” IEEE Trans. Antennas Propag. 51(8), 1690–1705 (2003).
[Crossref]

Hall, T. E.

D. M. Sheen, D. L. McMakin, and T. E. Hall, “Three-dimensional millimeter-wave imaging for concealed weapon detection,” IEEE Trans. Microwave Theory Tech. 49(9), 1581–1592 (2001).
[Crossref]

Hansen, P. C.

P. C. Hansen and D. P. O’Leary, “The use of the l-curve in the regularization of discrete ill-posed problems,” SIAM J. on Sci. Comput. 14(6), 1487–1503 (1993).
[Crossref]

P. C. Hansen, “Analysis of discrete ill-posed problems by means of the l-curve,” SIAM Rev. 34(4), 561–580 (1992).
[Crossref]

Hoorfar, A.

W. Zhang and A. Hoorfar, “Three-dimensional synthetic aperture radar imaging through multilayered walls,” IEEE Trans. Antennas Propag. 62(1), 459–462 (2014).
[Crossref]

K. M. Yemelyanov, N. Engheta, A. Hoorfar, and J. A. McVay, “Adaptive polarization contrast techniques for through-wall microwave imaging applications,” IEEE Trans. Geosci. Remote. Sens. 47(5), 1362–1374 (2009).
[Crossref]

C. Thajudeen and A. Hoorfar, “A comparative study of wall parameter estimation and mitigation techniques,” in 2010 USNC/URSI Meeting, (2010).

Huang, Q.

Q. Huang, L. Qu, B. Wu, and G. Fang, “Uwb through-wall imaging based on compressive sensing,” IEEE Trans. Geosci. Remote. Sens. 48(3), 1408–1415 (2010).
[Crossref]

Hunt, J.

T. Sleasman, M. F. Imani, W. Xu, J. Hunt, T. Driscoll, M. S. Reynolds, and D. R. Smith, “Waveguide-fed tunable metamaterial element for dynamic apertures,” IEEE Antennas Wirel. Propag. Lett. 15, 606–609 (2016).
[Crossref]

J. Hunt, J. Gollub, T. Driscoll, G. Lipworth, A. Mrozack, M. S. Reynolds, D. J. Brady, and D. R. Smith, “Metamaterial microwave holographic imaging system,” J. Opt. Soc. Am. A 31(10), 2109–2119 (2014).
[Crossref]

Imani, M. F.

M. Boyarsky, T. Sleasman, L. Pulido-Mancera, A. V. Diebold, M. F. Imani, and D. R. Smith, “Single-frequency 3d synthetic aperture imaging with dynamic metasurface antennas,” Appl. Opt. 57(15), 4123–4134 (2018).
[Crossref]

M. F. Imani, T. Sleasman, and D. R. Smith, “Two-dimensional dynamic metasurface apertures for computational microwave imaging,” IEEE Antennas Wirel. Propag. Lett. 17(12), 2299–2303 (2018).
[Crossref]

A. V. Diebold, M. F. Imani, T. Sleasman, and D. R. Smith, “Phaseless coherent and incoherent microwave ghost imaging with dynamic metasurface apertures,” Optica 5(12), 1529–1541 (2018).
[Crossref]

A. V. Diebold, M. F. Imani, T. Sleasman, and D. R. Smith, “Phaseless computational ghost imaging at microwave frequencies using a dynamic metasurface aperture,” Appl. Opt. 57(9), 2142–2149 (2018).
[Crossref]

A. V. Diebold, L. Pulido-Mancera, T. Sleasman, M. Boyarsky, M. F. Imani, and D. R. Smith, “Generalized range migration algorithm for synthetic aperture radar image reconstruction of metasurface antenna measurements,” J. Opt. Soc. Am. B 34(12), 2610–2623 (2017).
[Crossref]

T. Sleasman, M. Boyarsky, M. F. Imani, T. Fromenteze, J. N. Gollub, and D. R. Smith, “Single-frequency microwave imaging with dynamic metasurface apertures,” J. Opt. Soc. Am. B 34(8), 1713–1726 (2017).
[Crossref]

J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017).
[Crossref]

T. Sleasman, M. F. Imani, J. N. Gollub, and D. R. Smith, “Microwave imaging using a disordered cavity with a dynamically tunable impedance surface,” Phys. Rev. Appl. 6(5), 054019 (2016).
[Crossref]

T. Sleasman, M. F. Imani, W. Xu, J. Hunt, T. Driscoll, M. S. Reynolds, and D. R. Smith, “Waveguide-fed tunable metamaterial element for dynamic apertures,” IEEE Antennas Wirel. Propag. Lett. 15, 606–609 (2016).
[Crossref]

T. Sleasman, M. Boyarsky, M. F. Imani, J. Gollub, and D. Smith, “Design considerations for a dynamic metamaterial aperture for computational imaging at microwave frequencies,” J. Opt. Soc. Am. B 33(6), 1098–1111 (2016).
[Crossref]

L. Pulido-Mancera, T. Fromenteze, T. Sleasman, M. Boyarsky, M. F. Imani, M. Reynolds, and D. R. Smith, “Application of range migration algorithms to imaging with a dynamic metasurface antenna,” J. Opt. Soc. Am. B 33(10), 2082–2092 (2016).
[Crossref]

G. Lipworth, A. Rose, O. Yurduseven, V. R. Gowda, M. F. Imani, H. Odabasi, P. Trofatter, J. Gollub, and D. R. Smith, “Comprehensive simulation platform for a metamaterial imaging system,” Appl. Opt. 54(31), 9343–9353 (2015).
[Crossref]

T. Sleasman, M. F. Imani, J. N. Gollub, and D. R. Smith, “Dynamic metamaterial aperture for microwave imaging,” Appl. Phys. Lett. 107(20), 204104 (2015).
[Crossref]

T. Fromenteze, M. Boyarsky, J. Gollub, T. Sleasman, M. F. Imani, and D. R. Smith, “Single-frequency near-field mimo imaging,” in 2017 11th European Conference on Antennas and Propagation (EUCAP), (IEEE, 2017), pp. 1415–1418.

Jin, T.

T. Jin and A. Yarovoy, “A through-the-wall radar imaging method based on a realistic model,” Int. J. Antennas Propag. 2015, 1–8 (2015).
[Crossref]

Kassam, S. A.

F. Ahmad, M. G. Amin, and S. A. Kassam, “Synthetic aperture beamformer for imaging through a dielectric wall,” IEEE Trans. Aerosp. Electron. Syst. 41(1), 271–283 (2005).
[Crossref]

Kpre, E. L.

T. Fromenteze, E. L. Kpre, D. Carsenat, C. Decroze, and T. Sakamoto, “Single-shot compressive multiple-inputs multiple-outputs radar imaging using a two-port passive device,” IEEE Access 4, 1050–1060 (2016).
[Crossref]

Leigsnering, M.

M. Leigsnering, F. Ahmad, M. Amin, and A. Zoubir, “Multipath exploitation in through-the-wall radar imaging using sparse reconstruction,” IEEE Trans. Aerosp. Electron. Syst. 50(2), 920–939 (2014).
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L. Li, W. Zhang, and F. Li, “A novel autofocusing approach for real-time through-wall imaging under unknown wall characteristics,” IEEE Trans. Geosci. Remote. Sens. 48(1), 423–431 (2010).
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Li, L.

L. Li, W. Zhang, and F. Li, “A novel autofocusing approach for real-time through-wall imaging under unknown wall characteristics,” IEEE Trans. Geosci. Remote. Sens. 48(1), 423–431 (2010).
[Crossref]

Li, X.

E. J. Bond, X. Li, S. C. Hagness, and B. D. Van Veen, “Microwave imaging via space-time beamforming for early detection of breast cancer,” IEEE Trans. Antennas Propag. 51(8), 1690–1705 (2003).
[Crossref]

Lipworth, G.

J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017).
[Crossref]

G. Lipworth, A. Rose, O. Yurduseven, V. R. Gowda, M. F. Imani, H. Odabasi, P. Trofatter, J. Gollub, and D. R. Smith, “Comprehensive simulation platform for a metamaterial imaging system,” Appl. Opt. 54(31), 9343–9353 (2015).
[Crossref]

J. Hunt, J. Gollub, T. Driscoll, G. Lipworth, A. Mrozack, M. S. Reynolds, D. J. Brady, and D. R. Smith, “Metamaterial microwave holographic imaging system,” J. Opt. Soc. Am. A 31(10), 2109–2119 (2014).
[Crossref]

Liu, Q. H.

L.-P. Song, C. Yu, and Q. H. Liu, “Through-wall imaging (twi) by radar: 2-d tomographic results and analyses,” IEEE Trans. Geosci. Remote. Sens. 43(12), 2793–2798 (2005).
[Crossref]

Lopez-Sanchez, J. M.

J. M. Lopez-Sanchez and J. Fortuny-Guasch, “3-d radar imaging using range migration techniques,” IEEE Trans. Antennas Propag. 48(5), 728–737 (2000).
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Mandapati, G.

F. Ahmad, M. G. Amin, and G. Mandapati, “Autofocusing of through-the-wall radar imagery under unknown wall characteristics,” IEEE Trans. on Image Process. 16(7), 1785–1795 (2007).
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Marks, D. L.

D. L. Marks, O. Yurduseven, and D. R. Smith, “Sparse blind deconvolution for imaging through layered media,” Optica 4(12), 1514–1521 (2017).
[Crossref]

J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017).
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K. M. Yemelyanov, N. Engheta, A. Hoorfar, and J. A. McVay, “Adaptive polarization contrast techniques for through-wall microwave imaging applications,” IEEE Trans. Geosci. Remote. Sens. 47(5), 1362–1374 (2009).
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K. A. Michalski and J. R. Mosig, “Multilayered media green’s functions in integral equation formulations,” IEEE Trans. Antennas Propag. 45(3), 508–519 (1997).
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Mosig, J. R.

K. A. Michalski and J. R. Mosig, “Multilayered media green’s functions in integral equation formulations,” IEEE Trans. Antennas Propag. 45(3), 508–519 (1997).
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J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017).
[Crossref]

G. Lipworth, A. Rose, O. Yurduseven, V. R. Gowda, M. F. Imani, H. Odabasi, P. Trofatter, J. Gollub, and D. R. Smith, “Comprehensive simulation platform for a metamaterial imaging system,” Appl. Opt. 54(31), 9343–9353 (2015).
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J. Peabody, G. L. Charvat, J. Goodwin, and M. Tobias, “Through-wall imaging radar, Tech. rep.,” Massachusetts Institute of Technology-Lincoln Laboratory Lexington United States (2012).

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J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017).
[Crossref]

Phung, S. L.

V. H. Tang, A. Bouzerdoum, and S. L. Phung, “Multipolarization through-wall radar imaging using low-rank and jointly-sparse representations,” IEEE Trans. on Image Process. 27(4), 1763–1776 (2018).
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Pulido-Mancera, L.

Qu, L.

Q. Huang, L. Qu, B. Wu, and G. Fang, “Uwb through-wall imaging based on compressive sensing,” IEEE Trans. Geosci. Remote. Sens. 48(3), 1408–1415 (2010).
[Crossref]

Reynolds, M.

Reynolds, M. S.

J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017).
[Crossref]

T. Sleasman, M. F. Imani, W. Xu, J. Hunt, T. Driscoll, M. S. Reynolds, and D. R. Smith, “Waveguide-fed tunable metamaterial element for dynamic apertures,” IEEE Antennas Wirel. Propag. Lett. 15, 606–609 (2016).
[Crossref]

J. Hunt, J. Gollub, T. Driscoll, G. Lipworth, A. Mrozack, M. S. Reynolds, D. J. Brady, and D. R. Smith, “Metamaterial microwave holographic imaging system,” J. Opt. Soc. Am. A 31(10), 2109–2119 (2014).
[Crossref]

Rose, A.

J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017).
[Crossref]

G. Lipworth, A. Rose, O. Yurduseven, V. R. Gowda, M. F. Imani, H. Odabasi, P. Trofatter, J. Gollub, and D. R. Smith, “Comprehensive simulation platform for a metamaterial imaging system,” Appl. Opt. 54(31), 9343–9353 (2015).
[Crossref]

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T. Fromenteze, E. L. Kpre, D. Carsenat, C. Decroze, and T. Sakamoto, “Single-shot compressive multiple-inputs multiple-outputs radar imaging using a two-port passive device,” IEEE Access 4, 1050–1060 (2016).
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D. M. Sheen, D. L. McMakin, and T. E. Hall, “Three-dimensional millimeter-wave imaging for concealed weapon detection,” IEEE Trans. Microwave Theory Tech. 49(9), 1581–1592 (2001).
[Crossref]

Sleasman, T.

M. Boyarsky, T. Sleasman, L. Pulido-Mancera, A. V. Diebold, M. F. Imani, and D. R. Smith, “Single-frequency 3d synthetic aperture imaging with dynamic metasurface antennas,” Appl. Opt. 57(15), 4123–4134 (2018).
[Crossref]

A. V. Diebold, M. F. Imani, T. Sleasman, and D. R. Smith, “Phaseless computational ghost imaging at microwave frequencies using a dynamic metasurface aperture,” Appl. Opt. 57(9), 2142–2149 (2018).
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A. V. Diebold, M. F. Imani, T. Sleasman, and D. R. Smith, “Phaseless coherent and incoherent microwave ghost imaging with dynamic metasurface apertures,” Optica 5(12), 1529–1541 (2018).
[Crossref]

M. F. Imani, T. Sleasman, and D. R. Smith, “Two-dimensional dynamic metasurface apertures for computational microwave imaging,” IEEE Antennas Wirel. Propag. Lett. 17(12), 2299–2303 (2018).
[Crossref]

A. V. Diebold, L. Pulido-Mancera, T. Sleasman, M. Boyarsky, M. F. Imani, and D. R. Smith, “Generalized range migration algorithm for synthetic aperture radar image reconstruction of metasurface antenna measurements,” J. Opt. Soc. Am. B 34(12), 2610–2623 (2017).
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T. Sleasman, M. Boyarsky, M. F. Imani, T. Fromenteze, J. N. Gollub, and D. R. Smith, “Single-frequency microwave imaging with dynamic metasurface apertures,” J. Opt. Soc. Am. B 34(8), 1713–1726 (2017).
[Crossref]

J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017).
[Crossref]

T. Sleasman, M. F. Imani, J. N. Gollub, and D. R. Smith, “Microwave imaging using a disordered cavity with a dynamically tunable impedance surface,” Phys. Rev. Appl. 6(5), 054019 (2016).
[Crossref]

T. Sleasman, M. F. Imani, W. Xu, J. Hunt, T. Driscoll, M. S. Reynolds, and D. R. Smith, “Waveguide-fed tunable metamaterial element for dynamic apertures,” IEEE Antennas Wirel. Propag. Lett. 15, 606–609 (2016).
[Crossref]

T. Sleasman, M. Boyarsky, M. F. Imani, J. Gollub, and D. Smith, “Design considerations for a dynamic metamaterial aperture for computational imaging at microwave frequencies,” J. Opt. Soc. Am. B 33(6), 1098–1111 (2016).
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L. Pulido-Mancera, T. Fromenteze, T. Sleasman, M. Boyarsky, M. F. Imani, M. Reynolds, and D. R. Smith, “Application of range migration algorithms to imaging with a dynamic metasurface antenna,” J. Opt. Soc. Am. B 33(10), 2082–2092 (2016).
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T. Sleasman, M. F. Imani, J. N. Gollub, and D. R. Smith, “Dynamic metamaterial aperture for microwave imaging,” Appl. Phys. Lett. 107(20), 204104 (2015).
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T. Fromenteze, M. Boyarsky, J. Gollub, T. Sleasman, M. F. Imani, and D. R. Smith, “Single-frequency near-field mimo imaging,” in 2017 11th European Conference on Antennas and Propagation (EUCAP), (IEEE, 2017), pp. 1415–1418.

Smith, D.

Smith, D. R.

A. V. Diebold, M. F. Imani, T. Sleasman, and D. R. Smith, “Phaseless coherent and incoherent microwave ghost imaging with dynamic metasurface apertures,” Optica 5(12), 1529–1541 (2018).
[Crossref]

A. V. Diebold, M. F. Imani, T. Sleasman, and D. R. Smith, “Phaseless computational ghost imaging at microwave frequencies using a dynamic metasurface aperture,” Appl. Opt. 57(9), 2142–2149 (2018).
[Crossref]

M. F. Imani, T. Sleasman, and D. R. Smith, “Two-dimensional dynamic metasurface apertures for computational microwave imaging,” IEEE Antennas Wirel. Propag. Lett. 17(12), 2299–2303 (2018).
[Crossref]

M. Boyarsky, T. Sleasman, L. Pulido-Mancera, A. V. Diebold, M. F. Imani, and D. R. Smith, “Single-frequency 3d synthetic aperture imaging with dynamic metasurface antennas,” Appl. Opt. 57(15), 4123–4134 (2018).
[Crossref]

A. V. Diebold, L. Pulido-Mancera, T. Sleasman, M. Boyarsky, M. F. Imani, and D. R. Smith, “Generalized range migration algorithm for synthetic aperture radar image reconstruction of metasurface antenna measurements,” J. Opt. Soc. Am. B 34(12), 2610–2623 (2017).
[Crossref]

T. Sleasman, M. Boyarsky, M. F. Imani, T. Fromenteze, J. N. Gollub, and D. R. Smith, “Single-frequency microwave imaging with dynamic metasurface apertures,” J. Opt. Soc. Am. B 34(8), 1713–1726 (2017).
[Crossref]

J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017).
[Crossref]

D. L. Marks, O. Yurduseven, and D. R. Smith, “Sparse blind deconvolution for imaging through layered media,” Optica 4(12), 1514–1521 (2017).
[Crossref]

T. Sleasman, M. F. Imani, W. Xu, J. Hunt, T. Driscoll, M. S. Reynolds, and D. R. Smith, “Waveguide-fed tunable metamaterial element for dynamic apertures,” IEEE Antennas Wirel. Propag. Lett. 15, 606–609 (2016).
[Crossref]

T. Sleasman, M. F. Imani, J. N. Gollub, and D. R. Smith, “Microwave imaging using a disordered cavity with a dynamically tunable impedance surface,” Phys. Rev. Appl. 6(5), 054019 (2016).
[Crossref]

L. Pulido-Mancera, T. Fromenteze, T. Sleasman, M. Boyarsky, M. F. Imani, M. Reynolds, and D. R. Smith, “Application of range migration algorithms to imaging with a dynamic metasurface antenna,” J. Opt. Soc. Am. B 33(10), 2082–2092 (2016).
[Crossref]

G. Lipworth, A. Rose, O. Yurduseven, V. R. Gowda, M. F. Imani, H. Odabasi, P. Trofatter, J. Gollub, and D. R. Smith, “Comprehensive simulation platform for a metamaterial imaging system,” Appl. Opt. 54(31), 9343–9353 (2015).
[Crossref]

T. Sleasman, M. F. Imani, J. N. Gollub, and D. R. Smith, “Dynamic metamaterial aperture for microwave imaging,” Appl. Phys. Lett. 107(20), 204104 (2015).
[Crossref]

J. Hunt, J. Gollub, T. Driscoll, G. Lipworth, A. Mrozack, M. S. Reynolds, D. J. Brady, and D. R. Smith, “Metamaterial microwave holographic imaging system,” J. Opt. Soc. Am. A 31(10), 2109–2119 (2014).
[Crossref]

T. Fromenteze, M. Boyarsky, J. Gollub, T. Sleasman, M. F. Imani, and D. R. Smith, “Single-frequency near-field mimo imaging,” in 2017 11th European Conference on Antennas and Propagation (EUCAP), (IEEE, 2017), pp. 1415–1418.

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R. Solimene, I. Catapano, G. Gennarelli, A. Cuccaro, A. Dell’Aversano, and F. Soldovieri, “Sar imaging algorithms and some unconventional applications: A unified mathematical overview,” IEEE Signal Process. Mag. 31(4), 90–98 (2014).
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Solimene, R.

R. Solimene, I. Catapano, G. Gennarelli, A. Cuccaro, A. Dell’Aversano, and F. Soldovieri, “Sar imaging algorithms and some unconventional applications: A unified mathematical overview,” IEEE Signal Process. Mag. 31(4), 90–98 (2014).
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M. Dehmollaian, M. Thiel, and K. Sarabandi, “Through-the-wall imaging using differential sar,” IEEE Trans. Geosci. Remote. Sens. 47(5), 1289–1296 (2009).
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J. Peabody, G. L. Charvat, J. Goodwin, and M. Tobias, “Through-wall imaging radar, Tech. rep.,” Massachusetts Institute of Technology-Lincoln Laboratory Lexington United States (2012).

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J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017).
[Crossref]

Trofatter, P.

Van Veen, B. D.

E. J. Bond, X. Li, S. C. Hagness, and B. D. Van Veen, “Microwave imaging via space-time beamforming for early detection of breast cancer,” IEEE Trans. Antennas Propag. 51(8), 1690–1705 (2003).
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G. Wang and M. G. Amin, “Imaging through unknown walls using different standoff distances,” IEEE Trans. Signal Process. 54(10), 4015–4025 (2006).
[Crossref]

Wu, B.

Q. Huang, L. Qu, B. Wu, and G. Fang, “Uwb through-wall imaging based on compressive sensing,” IEEE Trans. Geosci. Remote. Sens. 48(3), 1408–1415 (2010).
[Crossref]

Xu, W.

T. Sleasman, M. F. Imani, W. Xu, J. Hunt, T. Driscoll, M. S. Reynolds, and D. R. Smith, “Waveguide-fed tunable metamaterial element for dynamic apertures,” IEEE Antennas Wirel. Propag. Lett. 15, 606–609 (2016).
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K. M. Yemelyanov, N. Engheta, A. Hoorfar, and J. A. McVay, “Adaptive polarization contrast techniques for through-wall microwave imaging applications,” IEEE Trans. Geosci. Remote. Sens. 47(5), 1362–1374 (2009).
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L.-P. Song, C. Yu, and Q. H. Liu, “Through-wall imaging (twi) by radar: 2-d tomographic results and analyses,” IEEE Trans. Geosci. Remote. Sens. 43(12), 2793–2798 (2005).
[Crossref]

Yurduseven, O.

D. L. Marks, O. Yurduseven, and D. R. Smith, “Sparse blind deconvolution for imaging through layered media,” Optica 4(12), 1514–1521 (2017).
[Crossref]

J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017).
[Crossref]

G. Lipworth, A. Rose, O. Yurduseven, V. R. Gowda, M. F. Imani, H. Odabasi, P. Trofatter, J. Gollub, and D. R. Smith, “Comprehensive simulation platform for a metamaterial imaging system,” Appl. Opt. 54(31), 9343–9353 (2015).
[Crossref]

Zhang, W.

W. Zhang and A. Hoorfar, “Three-dimensional synthetic aperture radar imaging through multilayered walls,” IEEE Trans. Antennas Propag. 62(1), 459–462 (2014).
[Crossref]

L. Li, W. Zhang, and F. Li, “A novel autofocusing approach for real-time through-wall imaging under unknown wall characteristics,” IEEE Trans. Geosci. Remote. Sens. 48(1), 423–431 (2010).
[Crossref]

Zoubir, A.

M. Leigsnering, F. Ahmad, M. Amin, and A. Zoubir, “Multipath exploitation in through-the-wall radar imaging using sparse reconstruction,” IEEE Trans. Aerosp. Electron. Syst. 50(2), 920–939 (2014).
[Crossref]

Zvolensky, T.

J. N. Gollub, O. Yurduseven, K. P. Trofatter, D. Arnitz, M. F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, T. Zvolensky, G. Lipworth, D. Brady, D. L. Marks, M. S. Reynolds, and D. R. Smith, “Large metasurface aperture for millimeter wave computational imaging at the human-scale,” Sci. Rep. 7(1), 42650 (2017).
[Crossref]

Appl. Opt. (3)

Appl. Phys. Lett. (1)

T. Sleasman, M. F. Imani, J. N. Gollub, and D. R. Smith, “Dynamic metamaterial aperture for microwave imaging,” Appl. Phys. Lett. 107(20), 204104 (2015).
[Crossref]

IEEE Access (1)

T. Fromenteze, E. L. Kpre, D. Carsenat, C. Decroze, and T. Sakamoto, “Single-shot compressive multiple-inputs multiple-outputs radar imaging using a two-port passive device,” IEEE Access 4, 1050–1060 (2016).
[Crossref]

IEEE Antennas Wirel. Propag. Lett. (2)

M. F. Imani, T. Sleasman, and D. R. Smith, “Two-dimensional dynamic metasurface apertures for computational microwave imaging,” IEEE Antennas Wirel. Propag. Lett. 17(12), 2299–2303 (2018).
[Crossref]

T. Sleasman, M. F. Imani, W. Xu, J. Hunt, T. Driscoll, M. S. Reynolds, and D. R. Smith, “Waveguide-fed tunable metamaterial element for dynamic apertures,” IEEE Antennas Wirel. Propag. Lett. 15, 606–609 (2016).
[Crossref]

IEEE Signal Process. Mag. (1)

R. Solimene, I. Catapano, G. Gennarelli, A. Cuccaro, A. Dell’Aversano, and F. Soldovieri, “Sar imaging algorithms and some unconventional applications: A unified mathematical overview,” IEEE Signal Process. Mag. 31(4), 90–98 (2014).
[Crossref]

IEEE Trans. Aerosp. Electron. Syst. (2)

M. Leigsnering, F. Ahmad, M. Amin, and A. Zoubir, “Multipath exploitation in through-the-wall radar imaging using sparse reconstruction,” IEEE Trans. Aerosp. Electron. Syst. 50(2), 920–939 (2014).
[Crossref]

F. Ahmad, M. G. Amin, and S. A. Kassam, “Synthetic aperture beamformer for imaging through a dielectric wall,” IEEE Trans. Aerosp. Electron. Syst. 41(1), 271–283 (2005).
[Crossref]

IEEE Trans. Antennas Propag. (6)

J. M. Lopez-Sanchez and J. Fortuny-Guasch, “3-d radar imaging using range migration techniques,” IEEE Trans. Antennas Propag. 48(5), 728–737 (2000).
[Crossref]

A. D. Yaghjian, “An overview of near-field antenna measurements,” IEEE Trans. Antennas Propag. 34(1), 30–45 (1986).
[Crossref]

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Figures (8)

Fig. 1.
Fig. 1. (a) A schematic of a microstrip-based dynamic metasurface antenna and (b) a close up view of its constituent resonant elements. A via connects the central region of the cELC to the bias line, which lies below the ground plane. The red and green diodes show the binary behavior by which the metamaterial elements can tune their interactions with the guided mode. (c) Employing two antennas allows for the creation of complex electric fields in the scene, as depicted by the multiplication of the Tx and Rx fields shown in blue-green. A wall is also depicted in (c) and can be seen to distort the fields.
Fig. 2.
Fig. 2. The imaging system with two dynamic metasurface apertures, a polystyrene wall, and objects arranged in a 2D scene.
Fig. 3.
Fig. 3. Imaging results of a pair of cylindrical objects, shown in (a), for the cases when there (b) no wall, (c) a wall but no clutter mitigation, and (d) the wall alone.
Fig. 4.
Fig. 4. Imaging results of a pair of scatterers after completing clutter mitigation with (a) method 1, traditional background subtraction, and (b) method 2, ensemble averaging.
Fig. 5.
Fig. 5. The geometry and parameters of the planar stratified TWI problem, used to calculate effective sources $\boldsymbol {\Psi }$ from the original sources $\boldsymbol {\Phi }$ .
Fig. 6.
Fig. 6. Imaging results for the bandwidth case (a-c) and monotone case (d-f). The images in (a) and (d) show the results without any wall; (b) and (e) show the results after clutter mitigation only; and (c) and (f) show the results after both clutter mitigation and wall compensation. The objects are shown schematically in (g).
Fig. 7.
Fig. 7. Point spread functions for the various cases are shown in (a-d), and cross sections through the objects location are shown in (e) and (f) for cross-range and range, respectively. (a) shows bandwidth with no compensation, (b) shows bandwidth with compensation, (c) shows monotone with no compensation, and (d) shows bandwidth with compensation.
Fig. 8.
Fig. 8. Imaging results for walls made of medium density fiberboard (a-b) and plywood (c-d). Bandwidth results are shown in (a) and (c), and monotone results are shown in (b) and (d). Objects for (a-b) and (c-d) are shown schematically in (e) and (f), respectively. Clutter mitigation is used in all cases, but no compensation for propagation through the wall is employed.

Equations (6)

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s 0 , f = ( Φ T , f + ) T G f Φ R , f +
σ ^ ( x , y ) = | F 2 D 1 ( f B S I , f ( k x , k y ) ) | .
Ψ T / R , f = Γ T / R , f Φ T / R , f .
γ ( Δ y , f ) E ( Δ y , f )
T 12 = 2 ϵ wall k 0 x ϵ wall k 0 x + k wall , x
E = cos ( θ ) T 12 T 23 e j ( k 0 p 1 + k wall p 2 + k 0 p 3 )

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