Abstract

We study mode selective up-conversion detection as a viable approach to improving signal-to-noise and ranging resolution in LIDAR applications. It involves pumping a nonlinear waveguide at the edge of phase matching with picosecond pulses, so that only the backscattered signal photons in a single or few desirable time-frequency modes are efficiently up-converted while the broadband background noise in all other modes is rejected. We demonstrate a 41-dB increase in the signal-to-noise ratio for single-photon counting compared to that of direct detection using a commercial InGaAs single-photon detector, while achieving sub-millimeter ranging resolution with few detected photons. The proposed technique implies new LIDAR capabilities for ranging and imaging.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

Precise optical time-of-flight (ToF) measurement is at the heart of light detection and ranging (LIDAR) systems. With bright laser pulses and diffraction-limited collimation optics, LIDAR has found a broad range of applications in remote sensing [1,2], terrestrial and environmental assessments [3,4], object recognition [5], and machine vision for autonomous vehicle navigation and mapping [6,7].

While earlier LIDAR systems rely on detecting bright optical pulses, in recent years both linear and Geiger mode single-photon detectors (SPDs) operating in various spectral regimes have been introduced to measure the backscattered signals at a single or few-photon level [8–11]. They can significantly extend the measurement range and increase spatial resolution for unprecedented LIDAR capabilities, although observing a trade off between the detection sensitivity and dynamic range. Notably, foliage penetrating LIDAR has been used to reveal fine topographical details in archaeological survey [12], and long range LIDAR is being adopted by the automotive industry edging toward level-5 fully autonomous driving [10].

While waveform-digitized avalanche photodiodes (APDs) in linear-mode have a detection threshold ∼ 1000 photons, LIDAR using Geiger mode SPDs is photon efficient in that a detection event can be sucessfully registered even when only a single backscattered photon reaches the APD [13]. However, to exploit such high sensitivity, false-alarming noise photon counts–which can arise from the ambient emission and intrinsic dark counts of the APD itself–need to be aggressively suppressed, so that the backscattered signal is statistically distinguishable from the noise. To this end, Silicon-based APDs can detect short-wavelength photons below 1.0 μm with high efficiency and low noise. However, LIDAR systems in this wavelength regime are overly restricted in the deployable optical power due to eye-hazard risks and also susceptible during daylight operation due to high solar background [5]. In contrast, long-wavelength LIDAR in the near-IR regime (between 1.0 μm and 2.0 μm) can benefit from much lower solar background and atmospheric attenuation, while exploiting an eye-safe window around 1.5 μm for high power operation. However, near-IR LIDAR is fundamentally constrained by the intrinsic dark counts of the InGaAs APDs, which is usually several orders of magnitude higher than the Si-APD even with aggressive measures for dark count suppressing. While those difficulties have recently shown to be addressed by using superconducting SPDs [14], their requirement of cryogenic cooling and meticulous temperature control, along with high cost and large volume, impede their uses in practical LIDAR applications [15].

To deploy LIDAR with eye-safe wavelengths while taking advantage of low solar background and reduced atmospheric scattering [16], up-conversion detection has emerged as a viable alternative by first transducing the near-IR photons to the visible and then detection using Si-APDs [2,17,18]. The up-conversion is typically accomplished through sum-frequency generation (SFG) in a χ(2) waveguide where a pump wave–either in continuous wave (CW) or pulse mode–is employed to translate the wavelength of the backscattered photons from near-IR to visible. For high conversion efficiency, the pump and signal are routinely prepared in narrowband spectral profiles well contained in the SFG phase matching band [2,17,18]. Oppositely, it has been shown that by using instead broadband pump pulses whose spectral width is comparable to the phase matching bandwidth, interesting phenomena can occur where only photons in a single time-frequency mode are converted efficiently but the photons in all other modes, including those spanning the exact same spectrum and time of arrival, are converted with much lower efficiency [19–21]. Recently, we have utilized this effect to significantly enhance the signal-to-noise ratio (SNR) for single-photon detection where a weak signal in a single mode is swamped by strong broadband noise randomly distributed in many modes [22].

In this work, we demonstrate how such mode-selective up-conversion can lead to improvement in LIDAR applications. With eye-safety and low solar background in mind, we use 1555.7 nm signal pulses and a periodically poled lithium niobate (PPLN) waveguide for the up-conversion. Unlike previous studies where inconvenient, far red-detuned pump lasers at around 2 micron are required to reduce Raman scattering noise [2], here we use pump pulses at 1545.3 nm to convert the signal to 775.3 nm. This is only feasible because the same mode-selectivity also substantially reduces the Raman noise [20,23]. Using both the pump and signal in the telecom band, we can take advantage of current compact laser and fiber components to realize a practical and cost-effective LIDAR systems. By approaching the phase matching bandwidth of the PPLN waveguide using picosecond optical pulses, we show that only photons in a single spatiotemporal mode, similar to the pump pulse shape in this case, are converted efficiently while background noise photons in all other modes are rejected [19,22], thereby exhibiting significantly enhanced measurement SNR compared to direct detection using an InGaAs-APD. We therefore call this system a parametric mode sorter detector (PMSD). It achieves a dynamic range of >63 dB and a total detection efficiency of 4.5% for single photons. The picosecond pulses also lead to remarkable ∼1 mm ranging resolution, which goes well beyond the limit of timing resolution for the Si-APD and electronic readout jitter of the entire system. We note that this resolution is achievable upon a single-photon detection event, which is in contrast to other approach relying on detecting bright signal pulses and post-processing.

2. Experimental setup

The experimental setup is shown in Fig. 1. Two nearly transform-limited, 6-ps pulses at 1555.7 nm and 1545.3 nm are spectrally carved from a high power femtosecond mode-locked laser (MLL, by Calmar Laser) by using a set of wavelength-division multiplexing (WDM) filters. For each wavelength, two WDM filters are cascaded to obtain >90 dB rejection of out-of-band noise photons, as shown in Fig. 1(a). The pulse repetition rate (PRF) is 50 MHz, which allows the maximum probing range of L = 3 m. This range can be increased to 300 m by either using a laser at 0.5 MHz PRF or using a pulse picker to reduce the PRF of the current MLL, given that the collimated laser beam diameter is sufficiently large for the Rayleigh length to cover the entire distance. The current LIDAR is based on a simple monostatic coaxial arrangement using off-the-shelf telecom-grade optical components. Collimated probe pulses in a Gaussian spatial mode (beam diameter: 2.2 mm) at 1555.7 nm are transmitted toward the target (a card board in this case), through a transceiver consisting of a fiber-to-free space collimator and 3-port fiber optic circulator, as shown in Fig. 1(b). An angle polished fiber connector and anti-reflection coated aspheric collimation lens are used to minimize the Fresnel reflection within the transceiver. The circulator separate the outgoing signal pulses and the incoming backscattered photons with minimum isolation of 55 dB. This bidirectional design reduces the number of optical components while simplifying the optical alignment, as the transmitter and receiver optics share a common optical axis. To quantify the noise suppression capability of the PMSD, we mix the backscattered photons with broadband noise by using a 50–50 fiber beam splitter, as shown in Fig. 1(c). The noise is created by filtering the amplified spontaneous emission (ASE) of an erbium doped fiber amplifier (EDFA). Its average power is adjusted through an in-line variable attenuator.

 figure: Fig. 1

Fig. 1 Experimental setup. (a) shows the creation of picoseconds pulses at 1555.7 nm and 1545.3 nm. (b) shows the propagation section. (c) illustrates the generation of broadband noise. (d) depicts the mode selective up-conversion system followed by a Si-APD. BS: beam splitter, DM: dichroic mirror, SPF: short pass filter, BPF: band pass filter.

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The mixed noise and backscattered photons are either sent to the InGaAS-APD for direct detection, or to the PPLN waveguide for PMSD. The InGaAs-APD is gated at 1 ns detection window and triggered by a 50-MHz reference signal from the MLL, with the gate delayed to coincide with the arrival of the backscattered photons from the target. The ToF measurement is limited by the timing jitter of the entire detection system, as

Δtdet=Δtlaser2+ΔtAPD2+ΔtTDC20.5ns
where Δtlaser is the laser pulse width (6 ps), ΔtAPD is the timing jitter of the InGaAS-APD (0.5 ns), and ΔtTDC is the timing resolution of the time-to-digital (TDC) converter (10 ps) (the timing jitter of the MLL is negligible). In this case, the total detection timing resolution is majorly limited by the InGaAS-APD, resulting in 7.5 cm ranging resolution.

For PMSD, the backscattered photons at 1555.7 nm and pump at 1545.3 nm are first passed through fiber polarization controllers (FPC) and then combined in a WDM filter before coupling into a 2-cm nonlinear waveguide (HC Photonics). A high resolution optical delay line (ODL) is used to temporally align the pump and the backscattered signal for ToF measurements. At the output, the up-converted photons are separated from the pump by using a dichroic mirror followed by a short pass filter. Those photons are further guided through one bandpass (extinction ratio: 25 dB) and two double-grating filters (extinction ratio: 45 dB each) with a center wavelength at 775.3 nm and total full-width half-maximum (FWHM) bandwidth of 1 nm. This eliminates any out-of-band noise photon including those from the second harmonic generation by the pump in the PPLN waveguide. Finally, the up-converted photons are detected by using a fiber coupled, ultralow noise (dark count rate: 1.8 Hz) Si-APD. The ranging resolution is about 0.9 mm, as dictated by the pump pulse width which is well shorter then the timing resolution of Si-APD and the electronic jitter of the entire system. This represents a 75 times improvement over the direct detection with InGaAs-APD. The key parameters of our experiments are listed in Table 1.

Tables Icon

Table 1. Key parameters of the PMSD and direct detection

3. Results

3.1. Pulse shape measurement

The pulse shape of the picoseconds probe and up-converting pump pulses carved out from the MLL with WDM filters is measured using a Frequency Resolved Optical Gating (FROG HR–150) with 0.1 ps resolution, Fig. 2. Both pulses are in nearly Gaussian shape with FWHM ∼5.8 and 6.1 ps at 1555.7 nm and 1545.3 nm, respectively. A secondary peak appearing 10 ps away from the main peak is due to non-ideal spectral filter shape of the WDM. The pulse energy of the probe and pump are measured to be 20 pJ and 13.6 pJ, respectively.

 figure: Fig. 2

Fig. 2 Retrieved pulse shapes by the FROG. (a) and (b) show amplitude and phase profile of generated pulses at 1555.7 nm and 1545.3 nm, respectively.

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3.2. Waveguide characterization

The phase matching curve of the 2-cm long magnesium doped PPLN waveguide is measured at 26.8 °C in the un-depleted pump regime. A continuous wave laser is coupled (overall coupling efficiency: 75%) along the transverse-electric polarization. Its waveguide is swept from 1548 nm to 1553 nm while the generated second harmonic power is measured. As shown in Fig. 3, the measurement data lie on top of an ideal sinc2 phase matching profile, with the 90-GHz FWHM phase matching centered at 1550.6 nm. The normalized internal conversion efficiency is measured to be 140% W−1cm−2. For the PMSD, ideal phase matching and high conversion efficiency are crucial for achieving good noise rejection with negligible Raman noise [22,24].

 figure: Fig. 3

Fig. 3 Phase matching profile of the PPLN waveguide, plotted against the frequency offset from 1550.6 nm. Blue dots show the experiment results with the waveguide temperature stabilized at 26.8 ± 0.1°C and the dashed line shows the sinc2 fitting. The FWHM phase matching bandwidth is about 90 GHz.

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3.3. Performance against noise

We compare the performance of the two detection systems in the presence of the broadband noise shown in Fig. 4, by completely blocking the backscattered photons from the target and varying the noise power by using an in-line variable attenuator. Figure 5(a) shows the number of photons per pulse as a function of the average noise power for the direct detection and the PMSD, respectively. In the limit of very low noise power, the noise photon count is 2.3×10−5 per pulse for the direct detection, but only 1.0×10−6 for PMSD, which amounts to a 14 dB improvement. The PMSD noise count is mainly from the Raman scattering, which can be additionally reduced by further detuning the pump from the signal. At higher noise power, the advantage of PMSD over direct detection is even more significantly. When the noise power reaches 10 nW, the PMSD outperforms the direct detection by as high as 43.6 dB, which implies gigantic advantage for LIDAR applications in complex environment with strong background light.

 figure: Fig. 4

Fig. 4 Broadband noise spectrum derived from ASE

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 figure: Fig. 5

Fig. 5 Performance against broadband noise. (a) compares the total detected noise photons by InGaAs detector and PMSD, and (b) compares the corresponding SNR.

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The overall detection efficiency is 7.5 % for direct detection with the InGaAs-APD and 4.5 % with the PMSD, the latter limited by the frequency conversion efficiency and optical transmission efficiency (total 30%), as well as quantum efficiency of the Si-APD (15%). Taking into account this overall detection efficiency penalty (2.2 dB), the PMSD can effectively improve the SNR by as much as 41.4 dB. To measure this, we next unblock the backscattered photons from the target while the noise source is switched off. The photon detection probabilities per pulse are measured 1.88 × 10−2 and 1.04 × 10−2 for the InGaAs-APD and the PMSD, respectively. The measured SNR in the presence of broadband noise is plotted in Fig. 5(b), which shows enhanced SNR by 41 dB for PMSD at high noise power. This confirms the distinct noise rejection advantage of the PMSD, especially in the presence of strong, broadband background noise.

The exceptional performance of PMSD roots in its strong noise rejection. For the direct detection, the total filter bandwidth B = 250 GHz (determined by the WDM) and detector time window T = 1 ns, for which the total number of detected time-frequency modes is πBT/2 ≈ 392 [25]. For PMSD, in contrast, the bandwidth is B ≈ 90 GHz as governed by phase matching, and the effective detection window T ≈ 6 ps, which is defined by the pump pulse duration. This reduces the number of detection modes to about 1, thus giving a noise suppression advantage of 26 dB. In principle, the same reduction can be achieved by using suitable time-frequency filters. However, it is quite challenging to implement in practice, especially when ultrashort pulses are used. Furthermore, working at the edge of phase matching, the current up-conversion can distinguish spectrally and temporally overlapping modes, giving rise to distinct mode selectivity. In [22], we have shown that it can gain >10 dB improvement in SNR over ideal matched filters. For the current settings, this mode-selectivity offers additional 12.5 dB noise suppression, thus giving a total of 38.5 dB advantage of SNR. Finally, as the up-conversion is efficient only for one polarization, the PMSD inherits another 3 dB enhancement by rejecting half of randomly polarized noise photons.

The maximum range of LIDAR systems is fundamentally limited as the detected photons from backscattering decrease rapidly for longer distance but the background noise level is nearly constant. For signal processing, the photon counting SNR, given by NBS/NBS+NBG, needs to be larger than 1 [26]. Here NBS and NBG are the backscattered signal photon counts and total background counts, respectively. By suppressing NBG, large SNR can still be obtained with very weak signal, thus extending the range of the LIDAR. For the direct detection, NBG attributes to ambient noise, probe laser noise, and the InGaAs-APD dark counts. For the PMSD, the same ambient and laser noise exist, but detection dark counts are mainly from the Raman noise generated in the up-conversion waveguide [18], as the Si-APD itself has an ultra-low dark count level. Discounting the ambient photons, NBG for the direct detection and the PMSD are measured to be 1000 and 50 per second, respectively, which amounts to 2 × 10−5 and 1 × 10−6 per pulse. In the opposite limit, the maximum photon rate for each system is restricted by the saturation of the SPD. For the direction detection, the InGaAs-APD saturates at Nmax = 1.0 × 105 per second. For the PMSD, Nmax = 1.4 × 107 which is limited only by the maximum count rate of the Si-APD. Those saturation count rates define the detection dynamic range as Nmax/NBSmin+NBG, where NBSmin is the minimum detectable backscattered signal photon counts for which SNR equals 1. In the absence of ambient noise photon, the direct detection and PMSD give dynamic range of 35 dB and 63 dB, respectively. This much improved dynamic range for the PMSD, when combined with its milimeter-range resolution, will enable high-precision imaging with greater depth of field. It implies substantial improvement for object identification and dynamic perception for LIDAR assisted machine vision, among other potential applications [27,28].

3.4. Resolution

To demonstrate the ranging resolution of the PMSD, we attempt to resolve the thickness of a ∼1 mm microscope glass slide by retrieving the ToF of Fresnel reflections at each of air-glass and glass-air interfaces. The schematic of the test setup is shown in Fig. 6, where a card board is placed at L1 = 890 mm and the glass slide with the measured thickness of t = 1.15 mm is placed in front of it. The distance between the rear surface of the glass and the card board is L2 = 6 mm. First, the backscattered photons are detected by PMSD without the glass. In Fig. 7, the detection rate is plotted as the temporal delay is scanned between the pump and backscattered photon pulses. The zero delay time T1 is defined at the location of the peak backscattered photon counts by the card board. The small peak at T2 = −10 ps is due to the side lobe in the up-converting pump, as seen in Fig. 2.

 figure: Fig. 6

Fig. 6 Schematic of the test setup for verifying the ranging resolution of the PMSD system.

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 figure: Fig. 7

Fig. 7 Characterization of ranging resolution. The solid blue and dashed red lines show the backscattering count rates as a function of the optical delay time without and with the glass, respectively. The photon counts in both cases are normalized by the maximum count rate at T=0 ps without the glass.

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Then, we repeat the measurement with the glass slide. The result is shown in Fig. 7, where the main peak, still by photon backscattering at the card board, is now shifted to T′1 = 3.48 ps. This shows that the optical path is effectively elongated by c(T′1T1) = 1 mm, which is expected as the photons now double pass a 1.15 mm glass with ∼1.5 refractive index. The other two peaks at T′4 = −46.63 ps and T′3 = −35.45 ps show the photons backscattered by the front and back surfaces of the glass, respectively. The distance between the glass and the card board is then derived as:

L2=c(T1T3)2=5.85mm.
Similarly the thickness of the glass is calculated as:
t=c(T3T4)2n=1.12mm.
Both are in good agreement with the measured values using a caliper. In addition, the surface reflectivity of the illuminated objects can be inferred by comparing the peak photon counts. For example, in Fig. 7 the reduced counts from the back surface of the glass slide and from the card board indicate 4% Fresnel reflections loss at glass/air interfaces and 1 dB absorption loss in the glass slide. This result suggests that the PMSD can resolve objects ∼1 mm away from each other while also providing information on their surface reflectivity. This milimetric ranging resolution is derived from the pico-second temporal resolution in the ToF measurement and is limited only by the pulse width of the pump and signal. It will prove useful for terrestrial mapping with extremely high resolution and three-dimensional machine vision with excellent depth of field [29,30]. In view of the speed limitation of the temporal scanning, a practical system may combine both direct detection and the PMSD to extend the probing range. Further improvement is possible by applying compressive or sparsity sampling techniques [29,31].

4. Conclusion

In conclusion, we have studied mode-selective up-conversion detection for improving the signal-to-noise performance and ranging resolution of a LIDAR system. By using a commercial lithium-niobate waveguide with 6 ps laser pulses for up-conversion, we demonstrate a 41 dB increase of the signal-to-noise ratio in single-photon detection compared to that of direct photon detection using a commercial InGaAs-avalanche photo diode. In addition, we achieve sub-millimeter ranging resolution by detecting only a few backscattered photons, which can be further improved by employing shorter pulses. As both the signal pulses and the pump pulses for up conversion are in telecom C-band, our system enjoys advantage of compact laser and fiber components to realize a practical and cost-effective system for various LIDAR applications. The exceptional noise suppression feature can be useful in weakly-illuminated systems, photon starved environments [32] or crosstalk and interference susceptible applications [33], with examples in identifying an object occluded by a highly reflective surface or surrounded by thick fog [34].

Funding

Office of Naval Research (ONR)(N00014-15-1-2393); EOIR Technologies (S16-06059).

Acknowledgment

This research is supported in part by the Office of Naval Research and by EOIR Technologies. Effort sponsored by the U.S. Government under Other Transaction number W15QKN-09-9-1001 between the National Warheads and Energetics Consortium and the Government. The US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Government.

References and links

1. M. A. Lefsky, W. B. Cohen, G. G. Parker, and D. J. Harding, “Lidar remote sensing for ecosystem studieslidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists,” BioScience 52, 19–30 (2002). [CrossRef]  

2. H. Xia, G. Shentu, M. Shangguan, X. Xia, X. Jia, C. Wang, J. Zhang, J. S. Pelc, M. M. Fejer, Q. Zhang, X. Dou, and J.-W. Pan, “Long-range micro-pulse aerosol lidar at 1.5 μm with an upconversion single-photon detector,” Opt. Lett. 40, 1579–1582 (2015). [CrossRef]   [PubMed]  

3. M. Alonzo, B. Bookhagen, J. P. McFadden, A. Sun, and D. A. Roberts, “Mapping urban forest leaf area index with airborne lidar using penetration metrics and allometry,” Remote. Sens. Environ. 162, 141–153 (2015). [CrossRef]  

4. M. Shangguan, H. Xia, C. Wang, J. Qiu, S. Lin, X. Dou, Q. Zhang, and J.-W. Pan, “Dual-frequency doppler lidar for wind detection with a superconducting nanowire single-photon detector,” Opt. Lett. 42, 3541–3544 (2017). [CrossRef]   [PubMed]  

5. A. McCarthy, X. Ren, A. D. Frera, N. R. Gemmell, N. J. Krichel, C. Scarcella, A. Ruggeri, A. Tosi, and G. S. Buller, “Kilometer-range depth imaging at 1550 nm wavelength using an ingaas/inp single-photon avalanche diode detector,” Opt. Express 21, 22098–22113 (2013). [CrossRef]   [PubMed]  

6. B. Schwarz, “Mapping the world in 3d,” Nat. Photonics 4, 429 (2010). [CrossRef]  

7. S. Sivaraman and M. M. Trivedi, “Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis,” IEEE Transactions on Intell. Transp. Syst. 14, 1773–1795 (2013). [CrossRef]  

8. M. P. A. Ullrich, “Linear lidar versus geiger-mode lidar: impact on data properties and data quality,” ProcSPIE 9832, 9832 (2016).

9. M. A. Itzler, U. Krishnamachari, M. Entwistle, X. Jiang, M. Owens, and K. Slomkowski, “Dark count statistics in geiger-mode avalanche photodiode cameras for 3-d imaging ladar,” IEEE J. Sel. Top. Quantum Electron. 20, 318–328 (2014). [CrossRef]  

10. W. E. Clifton, B. Steele, G. Nelson, A. Truscott, M. Itzler, and M. Entwistle, “Medium altitude airborne geiger-mode mapping lidar system,” ProcSPIE 9465, 9465 (2015).

11. B. Aull, “Geiger-mode avalanche photodiode arrays integrated to all-digital cmos circuits,” Sensors 16, 495 (2016). [CrossRef]   [PubMed]  

12. A. F. Chase, D. Z. Chase, C. T. Fisher, S. J. Leisz, and J. F. Weishampel, “Geospatial revolution and remote sensing lidar in mesoamerican archaeology,” Proc. Natl. Acad. Sci. 109, 12916–12921 (2012). [CrossRef]   [PubMed]  

13. M. D. Eisaman, J. Fan, A. Migdall, and S. V. Polyakov, “Invited review article: Single-photon sources and detectors,” Rev. Sci. Instruments 82, 071101 (2011). [CrossRef]  

14. A. E. Lita, A. J. Miller, and S. W. Nam, “Counting near-infrared single-photons with 95% efficiency,” Opt. Express 16, 3032–3040 (2008). [CrossRef]   [PubMed]  

15. J. Qiu, H. Xia, M. Shangguan, X. Dou, M. Li, C. Wang, X. Shang, S. Lin, and J. Liu, “Micro-pulse polarization lidar at 1.5 μm using a single superconducting nanowire single-photon detector,” Opt. Lett. 42, 4454–4457 (2017). [CrossRef]   [PubMed]  

16. A. Bucholtz, “Rayleigh-scattering calculations for the terrestrial atmosphere,” Appl. Opt. 34, 2765–2773 (1995). [CrossRef]  

17. F. Ma, M.-Y. Zheng, Q. Yao, X.-P. Xie, Q. Zhang, and J.-W. Pan, “1.064-μm-band up-conversion single-photon detector,” Opt. Express 25, 14558–14564 (2017). [CrossRef]   [PubMed]  

18. G.-L. Shentu, J. S. Pelc, X.-D. Wang, Q.-C. Sun, M.-Y. Zheng, M. M. Fejer, Q. Zhang, and J.-W. Pan, “Ultralow noise up-conversion detector and spectrometer for the telecom band,” Opt. Express 21, 13986–13991 (2013). [CrossRef]   [PubMed]  

19. A. Eckstein, B. Brecht, and C. Silberhorn, “A quantum pulse gate based on spectrally engineered sum frequency generation,” Opt. Express 19, 13770–13778 (2011). [CrossRef]   [PubMed]  

20. A. S. Kowligy, P. Manurkar, N. V. Corzo, V. G. Velev, M. Silver, R. P. Scott, S. J. B. Yoo, P. Kumar, G. S. Kanter, and Y.-P. Huang, “Quantum optical arbitrary waveform manipulation and measurement in real time,” Opt. Express 22, 27942–27957 (2014). [CrossRef]   [PubMed]  

21. P. Manurkar, N. Jain, M. Silver, Y.-P. Huang, C. Langrock, M. M. Fejer, P. Kumar, and G. S. Kanter, “Multidimensional mode-separable frequency conversion for high-speed quantum communication,” Optica 3, 1300–1307 (2016). [CrossRef]  

22. A. Shahverdi, Y. M. Sua, L. Tumeh, and Y.-P. Huang, “Quantum parametric mode sorting: Beating the time-frequency filtering,” Sci. Reports 7, 6495 (2017). [CrossRef]  

23. Y.-P. Huang and P. Kumar, “Distilling quantum entanglement via mode-matched filtering,” Phys. Rev. A 84, 032315 (2011). [CrossRef]  

24. C. R. Phillips, J. S. Pelc, and M. M. Fejer, “Parametric processes in quasi-phasematching gratings with random duty cycle errors,” J. Opt. Soc. Am. B 30, 982–993 (2013). [CrossRef]  

25. Y.-P. Huang and P. Kumar, “Distilling quantum entanglement via mode-matched filtering,” Phys. Rev. A 84, 032315 (2011). [CrossRef]  

26. S. Pellegrini, G. S. Buller, J. M. Smith, A. M. Wallace, and S. Cova, “Laser-based distance measurement using picosecond resolution time-correlated single-photon counting,” Meas. Sci. Technol. 11, 712 (2000). [CrossRef]  

27. M. Himmelsbach and A. Müller, “Lidar-based 3d object perception,” (2008).

28. A. Asvadi, C. Premebida, P. Peixoto, and U. Nunes, “3d lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes,” Robotics Auton. Syst. 83, 299–311 (2016). [CrossRef]  

29. M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016). Article. [CrossRef]  

30. D. Shin, A. Kirmani, V. K. Goyal, and J. H. Shapiro, “Photon-efficient computational 3-d and reflectivity imaging with single-photon detectors,” IEEE Transactions on Comput. Imaging 1, 112–125 (2015). [CrossRef]  

31. G. A. Howland, D. J. Lum, M. R. Ware, and J. C. Howell, “Photon counting compressive depth mapping,” Opt. Express 21, 23822–23837 (2013). [CrossRef]   [PubMed]  

32. Z. Zhang, J. Zhang, L. Wu, Y. Zhang, Y. Zhao, and J. Su, “Photon-counting chirped amplitude modulation lidar using a smart premixing method,” Opt. Lett. 38, 4389–4392 (2013). [CrossRef]   [PubMed]  

33. F. Zhang, P. Du, Q. Liu, M. Gong, and X. Fu, “Adaptive strategy for cppm single-photon collision avoidance lidar against dynamic crosstalk,” Opt. Express 25, 12237–12250 (2017). [CrossRef]   [PubMed]  

34. A. Kadambi, H. Zhao, B. Shi, and R. Raskar, “Occluded imaging with time-of-flight sensors,” ACM Trans. Graph. 35, 15 (2016). [CrossRef]  

References

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  1. M. A. Lefsky, W. B. Cohen, G. G. Parker, and D. J. Harding, “Lidar remote sensing for ecosystem studieslidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists,” BioScience 52, 19–30 (2002).
    [Crossref]
  2. H. Xia, G. Shentu, M. Shangguan, X. Xia, X. Jia, C. Wang, J. Zhang, J. S. Pelc, M. M. Fejer, Q. Zhang, X. Dou, and J.-W. Pan, “Long-range micro-pulse aerosol lidar at 1.5 μm with an upconversion single-photon detector,” Opt. Lett. 40, 1579–1582 (2015).
    [Crossref] [PubMed]
  3. M. Alonzo, B. Bookhagen, J. P. McFadden, A. Sun, and D. A. Roberts, “Mapping urban forest leaf area index with airborne lidar using penetration metrics and allometry,” Remote. Sens. Environ. 162, 141–153 (2015).
    [Crossref]
  4. M. Shangguan, H. Xia, C. Wang, J. Qiu, S. Lin, X. Dou, Q. Zhang, and J.-W. Pan, “Dual-frequency doppler lidar for wind detection with a superconducting nanowire single-photon detector,” Opt. Lett. 42, 3541–3544 (2017).
    [Crossref] [PubMed]
  5. A. McCarthy, X. Ren, A. D. Frera, N. R. Gemmell, N. J. Krichel, C. Scarcella, A. Ruggeri, A. Tosi, and G. S. Buller, “Kilometer-range depth imaging at 1550 nm wavelength using an ingaas/inp single-photon avalanche diode detector,” Opt. Express 21, 22098–22113 (2013).
    [Crossref] [PubMed]
  6. B. Schwarz, “Mapping the world in 3d,” Nat. Photonics 4, 429 (2010).
    [Crossref]
  7. S. Sivaraman and M. M. Trivedi, “Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis,” IEEE Transactions on Intell. Transp. Syst. 14, 1773–1795 (2013).
    [Crossref]
  8. M. P. A. Ullrich, “Linear lidar versus geiger-mode lidar: impact on data properties and data quality,” ProcSPIE 9832, 9832 (2016).
  9. M. A. Itzler, U. Krishnamachari, M. Entwistle, X. Jiang, M. Owens, and K. Slomkowski, “Dark count statistics in geiger-mode avalanche photodiode cameras for 3-d imaging ladar,” IEEE J. Sel. Top. Quantum Electron. 20, 318–328 (2014).
    [Crossref]
  10. W. E. Clifton, B. Steele, G. Nelson, A. Truscott, M. Itzler, and M. Entwistle, “Medium altitude airborne geiger-mode mapping lidar system,” ProcSPIE 9465, 9465 (2015).
  11. B. Aull, “Geiger-mode avalanche photodiode arrays integrated to all-digital cmos circuits,” Sensors 16, 495 (2016).
    [Crossref] [PubMed]
  12. A. F. Chase, D. Z. Chase, C. T. Fisher, S. J. Leisz, and J. F. Weishampel, “Geospatial revolution and remote sensing lidar in mesoamerican archaeology,” Proc. Natl. Acad. Sci. 109, 12916–12921 (2012).
    [Crossref] [PubMed]
  13. M. D. Eisaman, J. Fan, A. Migdall, and S. V. Polyakov, “Invited review article: Single-photon sources and detectors,” Rev. Sci. Instruments 82, 071101 (2011).
    [Crossref]
  14. A. E. Lita, A. J. Miller, and S. W. Nam, “Counting near-infrared single-photons with 95% efficiency,” Opt. Express 16, 3032–3040 (2008).
    [Crossref] [PubMed]
  15. J. Qiu, H. Xia, M. Shangguan, X. Dou, M. Li, C. Wang, X. Shang, S. Lin, and J. Liu, “Micro-pulse polarization lidar at 1.5 μm using a single superconducting nanowire single-photon detector,” Opt. Lett. 42, 4454–4457 (2017).
    [Crossref] [PubMed]
  16. A. Bucholtz, “Rayleigh-scattering calculations for the terrestrial atmosphere,” Appl. Opt. 34, 2765–2773 (1995).
    [Crossref]
  17. F. Ma, M.-Y. Zheng, Q. Yao, X.-P. Xie, Q. Zhang, and J.-W. Pan, “1.064-μm-band up-conversion single-photon detector,” Opt. Express 25, 14558–14564 (2017).
    [Crossref] [PubMed]
  18. G.-L. Shentu, J. S. Pelc, X.-D. Wang, Q.-C. Sun, M.-Y. Zheng, M. M. Fejer, Q. Zhang, and J.-W. Pan, “Ultralow noise up-conversion detector and spectrometer for the telecom band,” Opt. Express 21, 13986–13991 (2013).
    [Crossref] [PubMed]
  19. A. Eckstein, B. Brecht, and C. Silberhorn, “A quantum pulse gate based on spectrally engineered sum frequency generation,” Opt. Express 19, 13770–13778 (2011).
    [Crossref] [PubMed]
  20. A. S. Kowligy, P. Manurkar, N. V. Corzo, V. G. Velev, M. Silver, R. P. Scott, S. J. B. Yoo, P. Kumar, G. S. Kanter, and Y.-P. Huang, “Quantum optical arbitrary waveform manipulation and measurement in real time,” Opt. Express 22, 27942–27957 (2014).
    [Crossref] [PubMed]
  21. P. Manurkar, N. Jain, M. Silver, Y.-P. Huang, C. Langrock, M. M. Fejer, P. Kumar, and G. S. Kanter, “Multidimensional mode-separable frequency conversion for high-speed quantum communication,” Optica 3, 1300–1307 (2016).
    [Crossref]
  22. A. Shahverdi, Y. M. Sua, L. Tumeh, and Y.-P. Huang, “Quantum parametric mode sorting: Beating the time-frequency filtering,” Sci. Reports 7, 6495 (2017).
    [Crossref]
  23. Y.-P. Huang and P. Kumar, “Distilling quantum entanglement via mode-matched filtering,” Phys. Rev. A 84, 032315 (2011).
    [Crossref]
  24. C. R. Phillips, J. S. Pelc, and M. M. Fejer, “Parametric processes in quasi-phasematching gratings with random duty cycle errors,” J. Opt. Soc. Am. B 30, 982–993 (2013).
    [Crossref]
  25. Y.-P. Huang and P. Kumar, “Distilling quantum entanglement via mode-matched filtering,” Phys. Rev. A 84, 032315 (2011).
    [Crossref]
  26. S. Pellegrini, G. S. Buller, J. M. Smith, A. M. Wallace, and S. Cova, “Laser-based distance measurement using picosecond resolution time-correlated single-photon counting,” Meas. Sci. Technol. 11, 712 (2000).
    [Crossref]
  27. M. Himmelsbach and A. Müller, “Lidar-based 3d object perception,” (2008).
  28. A. Asvadi, C. Premebida, P. Peixoto, and U. Nunes, “3d lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes,” Robotics Auton. Syst. 83, 299–311 (2016).
    [Crossref]
  29. M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016). Article.
    [Crossref]
  30. D. Shin, A. Kirmani, V. K. Goyal, and J. H. Shapiro, “Photon-efficient computational 3-d and reflectivity imaging with single-photon detectors,” IEEE Transactions on Comput. Imaging 1, 112–125 (2015).
    [Crossref]
  31. G. A. Howland, D. J. Lum, M. R. Ware, and J. C. Howell, “Photon counting compressive depth mapping,” Opt. Express 21, 23822–23837 (2013).
    [Crossref] [PubMed]
  32. Z. Zhang, J. Zhang, L. Wu, Y. Zhang, Y. Zhao, and J. Su, “Photon-counting chirped amplitude modulation lidar using a smart premixing method,” Opt. Lett. 38, 4389–4392 (2013).
    [Crossref] [PubMed]
  33. F. Zhang, P. Du, Q. Liu, M. Gong, and X. Fu, “Adaptive strategy for cppm single-photon collision avoidance lidar against dynamic crosstalk,” Opt. Express 25, 12237–12250 (2017).
    [Crossref] [PubMed]
  34. A. Kadambi, H. Zhao, B. Shi, and R. Raskar, “Occluded imaging with time-of-flight sensors,” ACM Trans. Graph. 35, 15 (2016).
    [Crossref]

2017 (5)

2016 (6)

A. Kadambi, H. Zhao, B. Shi, and R. Raskar, “Occluded imaging with time-of-flight sensors,” ACM Trans. Graph. 35, 15 (2016).
[Crossref]

P. Manurkar, N. Jain, M. Silver, Y.-P. Huang, C. Langrock, M. M. Fejer, P. Kumar, and G. S. Kanter, “Multidimensional mode-separable frequency conversion for high-speed quantum communication,” Optica 3, 1300–1307 (2016).
[Crossref]

A. Asvadi, C. Premebida, P. Peixoto, and U. Nunes, “3d lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes,” Robotics Auton. Syst. 83, 299–311 (2016).
[Crossref]

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016). Article.
[Crossref]

B. Aull, “Geiger-mode avalanche photodiode arrays integrated to all-digital cmos circuits,” Sensors 16, 495 (2016).
[Crossref] [PubMed]

M. P. A. Ullrich, “Linear lidar versus geiger-mode lidar: impact on data properties and data quality,” ProcSPIE 9832, 9832 (2016).

2015 (4)

H. Xia, G. Shentu, M. Shangguan, X. Xia, X. Jia, C. Wang, J. Zhang, J. S. Pelc, M. M. Fejer, Q. Zhang, X. Dou, and J.-W. Pan, “Long-range micro-pulse aerosol lidar at 1.5 μm with an upconversion single-photon detector,” Opt. Lett. 40, 1579–1582 (2015).
[Crossref] [PubMed]

M. Alonzo, B. Bookhagen, J. P. McFadden, A. Sun, and D. A. Roberts, “Mapping urban forest leaf area index with airborne lidar using penetration metrics and allometry,” Remote. Sens. Environ. 162, 141–153 (2015).
[Crossref]

D. Shin, A. Kirmani, V. K. Goyal, and J. H. Shapiro, “Photon-efficient computational 3-d and reflectivity imaging with single-photon detectors,” IEEE Transactions on Comput. Imaging 1, 112–125 (2015).
[Crossref]

W. E. Clifton, B. Steele, G. Nelson, A. Truscott, M. Itzler, and M. Entwistle, “Medium altitude airborne geiger-mode mapping lidar system,” ProcSPIE 9465, 9465 (2015).

2014 (2)

A. S. Kowligy, P. Manurkar, N. V. Corzo, V. G. Velev, M. Silver, R. P. Scott, S. J. B. Yoo, P. Kumar, G. S. Kanter, and Y.-P. Huang, “Quantum optical arbitrary waveform manipulation and measurement in real time,” Opt. Express 22, 27942–27957 (2014).
[Crossref] [PubMed]

M. A. Itzler, U. Krishnamachari, M. Entwistle, X. Jiang, M. Owens, and K. Slomkowski, “Dark count statistics in geiger-mode avalanche photodiode cameras for 3-d imaging ladar,” IEEE J. Sel. Top. Quantum Electron. 20, 318–328 (2014).
[Crossref]

2013 (6)

2012 (1)

A. F. Chase, D. Z. Chase, C. T. Fisher, S. J. Leisz, and J. F. Weishampel, “Geospatial revolution and remote sensing lidar in mesoamerican archaeology,” Proc. Natl. Acad. Sci. 109, 12916–12921 (2012).
[Crossref] [PubMed]

2011 (4)

M. D. Eisaman, J. Fan, A. Migdall, and S. V. Polyakov, “Invited review article: Single-photon sources and detectors,” Rev. Sci. Instruments 82, 071101 (2011).
[Crossref]

A. Eckstein, B. Brecht, and C. Silberhorn, “A quantum pulse gate based on spectrally engineered sum frequency generation,” Opt. Express 19, 13770–13778 (2011).
[Crossref] [PubMed]

Y.-P. Huang and P. Kumar, “Distilling quantum entanglement via mode-matched filtering,” Phys. Rev. A 84, 032315 (2011).
[Crossref]

Y.-P. Huang and P. Kumar, “Distilling quantum entanglement via mode-matched filtering,” Phys. Rev. A 84, 032315 (2011).
[Crossref]

2010 (1)

B. Schwarz, “Mapping the world in 3d,” Nat. Photonics 4, 429 (2010).
[Crossref]

2008 (1)

2002 (1)

M. A. Lefsky, W. B. Cohen, G. G. Parker, and D. J. Harding, “Lidar remote sensing for ecosystem studieslidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists,” BioScience 52, 19–30 (2002).
[Crossref]

2000 (1)

S. Pellegrini, G. S. Buller, J. M. Smith, A. M. Wallace, and S. Cova, “Laser-based distance measurement using picosecond resolution time-correlated single-photon counting,” Meas. Sci. Technol. 11, 712 (2000).
[Crossref]

1995 (1)

Alonzo, M.

M. Alonzo, B. Bookhagen, J. P. McFadden, A. Sun, and D. A. Roberts, “Mapping urban forest leaf area index with airborne lidar using penetration metrics and allometry,” Remote. Sens. Environ. 162, 141–153 (2015).
[Crossref]

Asvadi, A.

A. Asvadi, C. Premebida, P. Peixoto, and U. Nunes, “3d lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes,” Robotics Auton. Syst. 83, 299–311 (2016).
[Crossref]

Aull, B.

B. Aull, “Geiger-mode avalanche photodiode arrays integrated to all-digital cmos circuits,” Sensors 16, 495 (2016).
[Crossref] [PubMed]

Bookhagen, B.

M. Alonzo, B. Bookhagen, J. P. McFadden, A. Sun, and D. A. Roberts, “Mapping urban forest leaf area index with airborne lidar using penetration metrics and allometry,” Remote. Sens. Environ. 162, 141–153 (2015).
[Crossref]

Brecht, B.

Bucholtz, A.

Buller, G. S.

A. McCarthy, X. Ren, A. D. Frera, N. R. Gemmell, N. J. Krichel, C. Scarcella, A. Ruggeri, A. Tosi, and G. S. Buller, “Kilometer-range depth imaging at 1550 nm wavelength using an ingaas/inp single-photon avalanche diode detector,” Opt. Express 21, 22098–22113 (2013).
[Crossref] [PubMed]

S. Pellegrini, G. S. Buller, J. M. Smith, A. M. Wallace, and S. Cova, “Laser-based distance measurement using picosecond resolution time-correlated single-photon counting,” Meas. Sci. Technol. 11, 712 (2000).
[Crossref]

Chase, A. F.

A. F. Chase, D. Z. Chase, C. T. Fisher, S. J. Leisz, and J. F. Weishampel, “Geospatial revolution and remote sensing lidar in mesoamerican archaeology,” Proc. Natl. Acad. Sci. 109, 12916–12921 (2012).
[Crossref] [PubMed]

Chase, D. Z.

A. F. Chase, D. Z. Chase, C. T. Fisher, S. J. Leisz, and J. F. Weishampel, “Geospatial revolution and remote sensing lidar in mesoamerican archaeology,” Proc. Natl. Acad. Sci. 109, 12916–12921 (2012).
[Crossref] [PubMed]

Clifton, W. E.

W. E. Clifton, B. Steele, G. Nelson, A. Truscott, M. Itzler, and M. Entwistle, “Medium altitude airborne geiger-mode mapping lidar system,” ProcSPIE 9465, 9465 (2015).

Cohen, W. B.

M. A. Lefsky, W. B. Cohen, G. G. Parker, and D. J. Harding, “Lidar remote sensing for ecosystem studieslidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists,” BioScience 52, 19–30 (2002).
[Crossref]

Corzo, N. V.

Cova, S.

S. Pellegrini, G. S. Buller, J. M. Smith, A. M. Wallace, and S. Cova, “Laser-based distance measurement using picosecond resolution time-correlated single-photon counting,” Meas. Sci. Technol. 11, 712 (2000).
[Crossref]

Dou, X.

Du, P.

Eckstein, A.

Edgar, M. P.

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016). Article.
[Crossref]

Eisaman, M. D.

M. D. Eisaman, J. Fan, A. Migdall, and S. V. Polyakov, “Invited review article: Single-photon sources and detectors,” Rev. Sci. Instruments 82, 071101 (2011).
[Crossref]

Entwistle, M.

W. E. Clifton, B. Steele, G. Nelson, A. Truscott, M. Itzler, and M. Entwistle, “Medium altitude airborne geiger-mode mapping lidar system,” ProcSPIE 9465, 9465 (2015).

M. A. Itzler, U. Krishnamachari, M. Entwistle, X. Jiang, M. Owens, and K. Slomkowski, “Dark count statistics in geiger-mode avalanche photodiode cameras for 3-d imaging ladar,” IEEE J. Sel. Top. Quantum Electron. 20, 318–328 (2014).
[Crossref]

Fan, J.

M. D. Eisaman, J. Fan, A. Migdall, and S. V. Polyakov, “Invited review article: Single-photon sources and detectors,” Rev. Sci. Instruments 82, 071101 (2011).
[Crossref]

Fejer, M. M.

Fisher, C. T.

A. F. Chase, D. Z. Chase, C. T. Fisher, S. J. Leisz, and J. F. Weishampel, “Geospatial revolution and remote sensing lidar in mesoamerican archaeology,” Proc. Natl. Acad. Sci. 109, 12916–12921 (2012).
[Crossref] [PubMed]

Frera, A. D.

Fu, X.

Gemmell, N. R.

Gibson, G. M.

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016). Article.
[Crossref]

Gong, M.

Goyal, V. K.

D. Shin, A. Kirmani, V. K. Goyal, and J. H. Shapiro, “Photon-efficient computational 3-d and reflectivity imaging with single-photon detectors,” IEEE Transactions on Comput. Imaging 1, 112–125 (2015).
[Crossref]

Harding, D. J.

M. A. Lefsky, W. B. Cohen, G. G. Parker, and D. J. Harding, “Lidar remote sensing for ecosystem studieslidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists,” BioScience 52, 19–30 (2002).
[Crossref]

Himmelsbach, M.

M. Himmelsbach and A. Müller, “Lidar-based 3d object perception,” (2008).

Howell, J. C.

Howland, G. A.

Huang, Y.-P.

A. Shahverdi, Y. M. Sua, L. Tumeh, and Y.-P. Huang, “Quantum parametric mode sorting: Beating the time-frequency filtering,” Sci. Reports 7, 6495 (2017).
[Crossref]

P. Manurkar, N. Jain, M. Silver, Y.-P. Huang, C. Langrock, M. M. Fejer, P. Kumar, and G. S. Kanter, “Multidimensional mode-separable frequency conversion for high-speed quantum communication,” Optica 3, 1300–1307 (2016).
[Crossref]

A. S. Kowligy, P. Manurkar, N. V. Corzo, V. G. Velev, M. Silver, R. P. Scott, S. J. B. Yoo, P. Kumar, G. S. Kanter, and Y.-P. Huang, “Quantum optical arbitrary waveform manipulation and measurement in real time,” Opt. Express 22, 27942–27957 (2014).
[Crossref] [PubMed]

Y.-P. Huang and P. Kumar, “Distilling quantum entanglement via mode-matched filtering,” Phys. Rev. A 84, 032315 (2011).
[Crossref]

Y.-P. Huang and P. Kumar, “Distilling quantum entanglement via mode-matched filtering,” Phys. Rev. A 84, 032315 (2011).
[Crossref]

Itzler, M.

W. E. Clifton, B. Steele, G. Nelson, A. Truscott, M. Itzler, and M. Entwistle, “Medium altitude airborne geiger-mode mapping lidar system,” ProcSPIE 9465, 9465 (2015).

Itzler, M. A.

M. A. Itzler, U. Krishnamachari, M. Entwistle, X. Jiang, M. Owens, and K. Slomkowski, “Dark count statistics in geiger-mode avalanche photodiode cameras for 3-d imaging ladar,” IEEE J. Sel. Top. Quantum Electron. 20, 318–328 (2014).
[Crossref]

Jain, N.

Jia, X.

Jiang, X.

M. A. Itzler, U. Krishnamachari, M. Entwistle, X. Jiang, M. Owens, and K. Slomkowski, “Dark count statistics in geiger-mode avalanche photodiode cameras for 3-d imaging ladar,” IEEE J. Sel. Top. Quantum Electron. 20, 318–328 (2014).
[Crossref]

Kadambi, A.

A. Kadambi, H. Zhao, B. Shi, and R. Raskar, “Occluded imaging with time-of-flight sensors,” ACM Trans. Graph. 35, 15 (2016).
[Crossref]

Kanter, G. S.

Kirmani, A.

D. Shin, A. Kirmani, V. K. Goyal, and J. H. Shapiro, “Photon-efficient computational 3-d and reflectivity imaging with single-photon detectors,” IEEE Transactions on Comput. Imaging 1, 112–125 (2015).
[Crossref]

Kowligy, A. S.

Krichel, N. J.

Krishnamachari, U.

M. A. Itzler, U. Krishnamachari, M. Entwistle, X. Jiang, M. Owens, and K. Slomkowski, “Dark count statistics in geiger-mode avalanche photodiode cameras for 3-d imaging ladar,” IEEE J. Sel. Top. Quantum Electron. 20, 318–328 (2014).
[Crossref]

Kumar, P.

Lamb, R.

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016). Article.
[Crossref]

Langrock, C.

Lefsky, M. A.

M. A. Lefsky, W. B. Cohen, G. G. Parker, and D. J. Harding, “Lidar remote sensing for ecosystem studieslidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists,” BioScience 52, 19–30 (2002).
[Crossref]

Leisz, S. J.

A. F. Chase, D. Z. Chase, C. T. Fisher, S. J. Leisz, and J. F. Weishampel, “Geospatial revolution and remote sensing lidar in mesoamerican archaeology,” Proc. Natl. Acad. Sci. 109, 12916–12921 (2012).
[Crossref] [PubMed]

Li, M.

Lin, S.

Lita, A. E.

Liu, J.

Liu, Q.

Lum, D. J.

Ma, F.

Manurkar, P.

McCarthy, A.

McFadden, J. P.

M. Alonzo, B. Bookhagen, J. P. McFadden, A. Sun, and D. A. Roberts, “Mapping urban forest leaf area index with airborne lidar using penetration metrics and allometry,” Remote. Sens. Environ. 162, 141–153 (2015).
[Crossref]

Migdall, A.

M. D. Eisaman, J. Fan, A. Migdall, and S. V. Polyakov, “Invited review article: Single-photon sources and detectors,” Rev. Sci. Instruments 82, 071101 (2011).
[Crossref]

Miller, A. J.

Müller, A.

M. Himmelsbach and A. Müller, “Lidar-based 3d object perception,” (2008).

Nam, S. W.

Nelson, G.

W. E. Clifton, B. Steele, G. Nelson, A. Truscott, M. Itzler, and M. Entwistle, “Medium altitude airborne geiger-mode mapping lidar system,” ProcSPIE 9465, 9465 (2015).

Nunes, U.

A. Asvadi, C. Premebida, P. Peixoto, and U. Nunes, “3d lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes,” Robotics Auton. Syst. 83, 299–311 (2016).
[Crossref]

Owens, M.

M. A. Itzler, U. Krishnamachari, M. Entwistle, X. Jiang, M. Owens, and K. Slomkowski, “Dark count statistics in geiger-mode avalanche photodiode cameras for 3-d imaging ladar,” IEEE J. Sel. Top. Quantum Electron. 20, 318–328 (2014).
[Crossref]

Padgett, M. J.

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016). Article.
[Crossref]

Pan, J.-W.

Parker, G. G.

M. A. Lefsky, W. B. Cohen, G. G. Parker, and D. J. Harding, “Lidar remote sensing for ecosystem studieslidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists,” BioScience 52, 19–30 (2002).
[Crossref]

Peixoto, P.

A. Asvadi, C. Premebida, P. Peixoto, and U. Nunes, “3d lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes,” Robotics Auton. Syst. 83, 299–311 (2016).
[Crossref]

Pelc, J. S.

Pellegrini, S.

S. Pellegrini, G. S. Buller, J. M. Smith, A. M. Wallace, and S. Cova, “Laser-based distance measurement using picosecond resolution time-correlated single-photon counting,” Meas. Sci. Technol. 11, 712 (2000).
[Crossref]

Phillips, C. R.

Polyakov, S. V.

M. D. Eisaman, J. Fan, A. Migdall, and S. V. Polyakov, “Invited review article: Single-photon sources and detectors,” Rev. Sci. Instruments 82, 071101 (2011).
[Crossref]

Premebida, C.

A. Asvadi, C. Premebida, P. Peixoto, and U. Nunes, “3d lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes,” Robotics Auton. Syst. 83, 299–311 (2016).
[Crossref]

Qiu, J.

Radwell, N.

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016). Article.
[Crossref]

Raskar, R.

A. Kadambi, H. Zhao, B. Shi, and R. Raskar, “Occluded imaging with time-of-flight sensors,” ACM Trans. Graph. 35, 15 (2016).
[Crossref]

Ren, X.

Roberts, D. A.

M. Alonzo, B. Bookhagen, J. P. McFadden, A. Sun, and D. A. Roberts, “Mapping urban forest leaf area index with airborne lidar using penetration metrics and allometry,” Remote. Sens. Environ. 162, 141–153 (2015).
[Crossref]

Ruggeri, A.

Scarcella, C.

Schwarz, B.

B. Schwarz, “Mapping the world in 3d,” Nat. Photonics 4, 429 (2010).
[Crossref]

Scott, R. P.

Shahverdi, A.

A. Shahverdi, Y. M. Sua, L. Tumeh, and Y.-P. Huang, “Quantum parametric mode sorting: Beating the time-frequency filtering,” Sci. Reports 7, 6495 (2017).
[Crossref]

Shang, X.

Shangguan, M.

Shapiro, J. H.

D. Shin, A. Kirmani, V. K. Goyal, and J. H. Shapiro, “Photon-efficient computational 3-d and reflectivity imaging with single-photon detectors,” IEEE Transactions on Comput. Imaging 1, 112–125 (2015).
[Crossref]

Shentu, G.

Shentu, G.-L.

Shi, B.

A. Kadambi, H. Zhao, B. Shi, and R. Raskar, “Occluded imaging with time-of-flight sensors,” ACM Trans. Graph. 35, 15 (2016).
[Crossref]

Shin, D.

D. Shin, A. Kirmani, V. K. Goyal, and J. H. Shapiro, “Photon-efficient computational 3-d and reflectivity imaging with single-photon detectors,” IEEE Transactions on Comput. Imaging 1, 112–125 (2015).
[Crossref]

Silberhorn, C.

Silver, M.

Sivaraman, S.

S. Sivaraman and M. M. Trivedi, “Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis,” IEEE Transactions on Intell. Transp. Syst. 14, 1773–1795 (2013).
[Crossref]

Slomkowski, K.

M. A. Itzler, U. Krishnamachari, M. Entwistle, X. Jiang, M. Owens, and K. Slomkowski, “Dark count statistics in geiger-mode avalanche photodiode cameras for 3-d imaging ladar,” IEEE J. Sel. Top. Quantum Electron. 20, 318–328 (2014).
[Crossref]

Smith, J. M.

S. Pellegrini, G. S. Buller, J. M. Smith, A. M. Wallace, and S. Cova, “Laser-based distance measurement using picosecond resolution time-correlated single-photon counting,” Meas. Sci. Technol. 11, 712 (2000).
[Crossref]

Steele, B.

W. E. Clifton, B. Steele, G. Nelson, A. Truscott, M. Itzler, and M. Entwistle, “Medium altitude airborne geiger-mode mapping lidar system,” ProcSPIE 9465, 9465 (2015).

Su, J.

Sua, Y. M.

A. Shahverdi, Y. M. Sua, L. Tumeh, and Y.-P. Huang, “Quantum parametric mode sorting: Beating the time-frequency filtering,” Sci. Reports 7, 6495 (2017).
[Crossref]

Sun, A.

M. Alonzo, B. Bookhagen, J. P. McFadden, A. Sun, and D. A. Roberts, “Mapping urban forest leaf area index with airborne lidar using penetration metrics and allometry,” Remote. Sens. Environ. 162, 141–153 (2015).
[Crossref]

Sun, B.

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016). Article.
[Crossref]

Sun, M.-J.

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016). Article.
[Crossref]

Sun, Q.-C.

Tosi, A.

Trivedi, M. M.

S. Sivaraman and M. M. Trivedi, “Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis,” IEEE Transactions on Intell. Transp. Syst. 14, 1773–1795 (2013).
[Crossref]

Truscott, A.

W. E. Clifton, B. Steele, G. Nelson, A. Truscott, M. Itzler, and M. Entwistle, “Medium altitude airborne geiger-mode mapping lidar system,” ProcSPIE 9465, 9465 (2015).

Tumeh, L.

A. Shahverdi, Y. M. Sua, L. Tumeh, and Y.-P. Huang, “Quantum parametric mode sorting: Beating the time-frequency filtering,” Sci. Reports 7, 6495 (2017).
[Crossref]

Ullrich, M. P. A.

M. P. A. Ullrich, “Linear lidar versus geiger-mode lidar: impact on data properties and data quality,” ProcSPIE 9832, 9832 (2016).

Velev, V. G.

Wallace, A. M.

S. Pellegrini, G. S. Buller, J. M. Smith, A. M. Wallace, and S. Cova, “Laser-based distance measurement using picosecond resolution time-correlated single-photon counting,” Meas. Sci. Technol. 11, 712 (2000).
[Crossref]

Wang, C.

Wang, X.-D.

Ware, M. R.

Weishampel, J. F.

A. F. Chase, D. Z. Chase, C. T. Fisher, S. J. Leisz, and J. F. Weishampel, “Geospatial revolution and remote sensing lidar in mesoamerican archaeology,” Proc. Natl. Acad. Sci. 109, 12916–12921 (2012).
[Crossref] [PubMed]

Wu, L.

Xia, H.

Xia, X.

Xie, X.-P.

Yao, Q.

Yoo, S. J. B.

Zhang, F.

Zhang, J.

Zhang, Q.

Zhang, Y.

Zhang, Z.

Zhao, H.

A. Kadambi, H. Zhao, B. Shi, and R. Raskar, “Occluded imaging with time-of-flight sensors,” ACM Trans. Graph. 35, 15 (2016).
[Crossref]

Zhao, Y.

Zheng, M.-Y.

ACM Trans. Graph. (1)

A. Kadambi, H. Zhao, B. Shi, and R. Raskar, “Occluded imaging with time-of-flight sensors,” ACM Trans. Graph. 35, 15 (2016).
[Crossref]

Appl. Opt. (1)

BioScience (1)

M. A. Lefsky, W. B. Cohen, G. G. Parker, and D. J. Harding, “Lidar remote sensing for ecosystem studieslidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists,” BioScience 52, 19–30 (2002).
[Crossref]

IEEE J. Sel. Top. Quantum Electron. (1)

M. A. Itzler, U. Krishnamachari, M. Entwistle, X. Jiang, M. Owens, and K. Slomkowski, “Dark count statistics in geiger-mode avalanche photodiode cameras for 3-d imaging ladar,” IEEE J. Sel. Top. Quantum Electron. 20, 318–328 (2014).
[Crossref]

IEEE Transactions on Comput. Imaging (1)

D. Shin, A. Kirmani, V. K. Goyal, and J. H. Shapiro, “Photon-efficient computational 3-d and reflectivity imaging with single-photon detectors,” IEEE Transactions on Comput. Imaging 1, 112–125 (2015).
[Crossref]

IEEE Transactions on Intell. Transp. Syst. (1)

S. Sivaraman and M. M. Trivedi, “Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis,” IEEE Transactions on Intell. Transp. Syst. 14, 1773–1795 (2013).
[Crossref]

J. Opt. Soc. Am. B (1)

Meas. Sci. Technol. (1)

S. Pellegrini, G. S. Buller, J. M. Smith, A. M. Wallace, and S. Cova, “Laser-based distance measurement using picosecond resolution time-correlated single-photon counting,” Meas. Sci. Technol. 11, 712 (2000).
[Crossref]

Nat. Commun. (1)

M.-J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016). Article.
[Crossref]

Nat. Photonics (1)

B. Schwarz, “Mapping the world in 3d,” Nat. Photonics 4, 429 (2010).
[Crossref]

Opt. Express (8)

G. A. Howland, D. J. Lum, M. R. Ware, and J. C. Howell, “Photon counting compressive depth mapping,” Opt. Express 21, 23822–23837 (2013).
[Crossref] [PubMed]

F. Zhang, P. Du, Q. Liu, M. Gong, and X. Fu, “Adaptive strategy for cppm single-photon collision avoidance lidar against dynamic crosstalk,” Opt. Express 25, 12237–12250 (2017).
[Crossref] [PubMed]

A. McCarthy, X. Ren, A. D. Frera, N. R. Gemmell, N. J. Krichel, C. Scarcella, A. Ruggeri, A. Tosi, and G. S. Buller, “Kilometer-range depth imaging at 1550 nm wavelength using an ingaas/inp single-photon avalanche diode detector,” Opt. Express 21, 22098–22113 (2013).
[Crossref] [PubMed]

F. Ma, M.-Y. Zheng, Q. Yao, X.-P. Xie, Q. Zhang, and J.-W. Pan, “1.064-μm-band up-conversion single-photon detector,” Opt. Express 25, 14558–14564 (2017).
[Crossref] [PubMed]

G.-L. Shentu, J. S. Pelc, X.-D. Wang, Q.-C. Sun, M.-Y. Zheng, M. M. Fejer, Q. Zhang, and J.-W. Pan, “Ultralow noise up-conversion detector and spectrometer for the telecom band,” Opt. Express 21, 13986–13991 (2013).
[Crossref] [PubMed]

A. Eckstein, B. Brecht, and C. Silberhorn, “A quantum pulse gate based on spectrally engineered sum frequency generation,” Opt. Express 19, 13770–13778 (2011).
[Crossref] [PubMed]

A. S. Kowligy, P. Manurkar, N. V. Corzo, V. G. Velev, M. Silver, R. P. Scott, S. J. B. Yoo, P. Kumar, G. S. Kanter, and Y.-P. Huang, “Quantum optical arbitrary waveform manipulation and measurement in real time,” Opt. Express 22, 27942–27957 (2014).
[Crossref] [PubMed]

A. E. Lita, A. J. Miller, and S. W. Nam, “Counting near-infrared single-photons with 95% efficiency,” Opt. Express 16, 3032–3040 (2008).
[Crossref] [PubMed]

Opt. Lett. (4)

Optica (1)

Phys. Rev. A (2)

Y.-P. Huang and P. Kumar, “Distilling quantum entanglement via mode-matched filtering,” Phys. Rev. A 84, 032315 (2011).
[Crossref]

Y.-P. Huang and P. Kumar, “Distilling quantum entanglement via mode-matched filtering,” Phys. Rev. A 84, 032315 (2011).
[Crossref]

Proc. Natl. Acad. Sci. (1)

A. F. Chase, D. Z. Chase, C. T. Fisher, S. J. Leisz, and J. F. Weishampel, “Geospatial revolution and remote sensing lidar in mesoamerican archaeology,” Proc. Natl. Acad. Sci. 109, 12916–12921 (2012).
[Crossref] [PubMed]

ProcSPIE (2)

M. P. A. Ullrich, “Linear lidar versus geiger-mode lidar: impact on data properties and data quality,” ProcSPIE 9832, 9832 (2016).

W. E. Clifton, B. Steele, G. Nelson, A. Truscott, M. Itzler, and M. Entwistle, “Medium altitude airborne geiger-mode mapping lidar system,” ProcSPIE 9465, 9465 (2015).

Remote. Sens. Environ. (1)

M. Alonzo, B. Bookhagen, J. P. McFadden, A. Sun, and D. A. Roberts, “Mapping urban forest leaf area index with airborne lidar using penetration metrics and allometry,” Remote. Sens. Environ. 162, 141–153 (2015).
[Crossref]

Rev. Sci. Instruments (1)

M. D. Eisaman, J. Fan, A. Migdall, and S. V. Polyakov, “Invited review article: Single-photon sources and detectors,” Rev. Sci. Instruments 82, 071101 (2011).
[Crossref]

Robotics Auton. Syst. (1)

A. Asvadi, C. Premebida, P. Peixoto, and U. Nunes, “3d lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes,” Robotics Auton. Syst. 83, 299–311 (2016).
[Crossref]

Sci. Reports (1)

A. Shahverdi, Y. M. Sua, L. Tumeh, and Y.-P. Huang, “Quantum parametric mode sorting: Beating the time-frequency filtering,” Sci. Reports 7, 6495 (2017).
[Crossref]

Sensors (1)

B. Aull, “Geiger-mode avalanche photodiode arrays integrated to all-digital cmos circuits,” Sensors 16, 495 (2016).
[Crossref] [PubMed]

Other (1)

M. Himmelsbach and A. Müller, “Lidar-based 3d object perception,” (2008).

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Figures (7)

Fig. 1
Fig. 1 Experimental setup. (a) shows the creation of picoseconds pulses at 1555.7 nm and 1545.3 nm. (b) shows the propagation section. (c) illustrates the generation of broadband noise. (d) depicts the mode selective up-conversion system followed by a Si-APD. BS: beam splitter, DM: dichroic mirror, SPF: short pass filter, BPF: band pass filter.
Fig. 2
Fig. 2 Retrieved pulse shapes by the FROG. (a) and (b) show amplitude and phase profile of generated pulses at 1555.7 nm and 1545.3 nm, respectively.
Fig. 3
Fig. 3 Phase matching profile of the PPLN waveguide, plotted against the frequency offset from 1550.6 nm. Blue dots show the experiment results with the waveguide temperature stabilized at 26.8 ± 0.1°C and the dashed line shows the sinc2 fitting. The FWHM phase matching bandwidth is about 90 GHz.
Fig. 4
Fig. 4 Broadband noise spectrum derived from ASE
Fig. 5
Fig. 5 Performance against broadband noise. (a) compares the total detected noise photons by InGaAs detector and PMSD, and (b) compares the corresponding SNR.
Fig. 6
Fig. 6 Schematic of the test setup for verifying the ranging resolution of the PMSD system.
Fig. 7
Fig. 7 Characterization of ranging resolution. The solid blue and dashed red lines show the backscattering count rates as a function of the optical delay time without and with the glass, respectively. The photon counts in both cases are normalized by the maximum count rate at T=0 ps without the glass.

Tables (1)

Tables Icon

Table 1 Key parameters of the PMSD and direct detection

Equations (3)

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Δ t det = Δ t laser 2 + Δ t APD 2 + Δ t TDC 2 0.5 ns
L 2 = c ( T 1 T 3 ) 2 = 5.85 m m .
t = c ( T 3 T 4 ) 2 n = 1.12 m m .

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