Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Continuous wavelet transform and iterative decrement algorithm for the Lidar full-waveform echo decomposition

Not Accessible

Your library or personal account may give you access

Abstract

In this paper, we propose a continuous wavelet transform and iterative decrement algorithm to decompose the light detection and ranging (LiDAR) full-waveform echoes into a series of components, each of which can be assumed as Gaussian essentially. We calculate the scale of continuous wavelet transform in real time according to the relationship between the center frequency of the mother wavelet and the approximate frequency of the transmitted laser pulse. The approximated frequency is calculated according to the half-width of the effective part of transmitted laser pulse. The positions of the Gaussian model components in the echoes can be precisely predicted according to the positions of the maxima of the continuous wavelet transform coefficient. And the boundary points which locate at the left and right sides of the position of the detected components can be detected. Then, the effective sections can be clipped according to the positions of the boundary points. In order to detect the hidden components which are obscured by the high responses from their adjacent components and estimate the initial parameters, the iterative decrement algorithm is carried out. The initial parameters are fitted by the Levenberg–Marquardt algorithm. In order to verify the proposed method, the simulations and experiments whose data is recorded by our coding LiDAR have been done. The simulations and experiments results indicate that the proposed method exhibits excellent performances, and it is valid for the complex full-waveform echo, which includes serious overlapping components.

© 2019 Optical Society of America

Full Article  |  PDF Article
More Like This
Lidar full-waveform decomposition based on empirical mode decomposition and local-Levenberg–Marquard fitting

Wu Qinqin, Qiang Shengzhi, Wang Yuanqing, and Ren Shuping
Appl. Opt. 58(29) 7943-7949 (2019)

Full-waveform LiDAR echo decomposition based on dense and residual neural networks

Gangping Liu and Jun Ke
Appl. Opt. 61(9) F15-F24 (2022)

Echo decomposition of full-waveform LiDAR based on a digital implicit model and a particle swarm optimization

Ruiqiang Chen, Haiyi Bian, Chunyan Hou, Li Fang, and Ou Zhang
Appl. Opt. 59(13) 4030-4039 (2020)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (10)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (4)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (19)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved