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

FMCW lidar multitarget detection based on skeleton tree waveform matching

Not Accessible

Your library or personal account may give you access

Abstract

Frequency-modulated continuous-wave lidar realizes 4D (three-dimensional space and velocity) imaging of the scene by emitting positive and negative frequency sweep laser signals. The premise of it is to identify the frequency points corresponding to the same target in the positive and negative sweep echo signals. For dechirp receiving, there is usually one peak in the frequency spectrum of the positive and negative sweep signals, respectively. Therefore, it is easy to identify and match the peaks. But in a complex environment, the laser beam will irradiate multiple targets at the same time. In addition, beam scanning and target motion cause the echo spectrum to broaden. The above reasons make it extremely difficult to identify and match peaks in practice. To solve this problem, the waveform-matching algorithm based on the skeleton tree is first applied to multitarget echo pairing. The basic idea of the algorithm is to quantify the target echo hierarchically to generate a skeleton tree. The generation of nodes is based on the relative amplitude of waveform peaks and reflects the characteristics of wave crests nesting. Then the similarity of the signal is determined by comparing the distance between the two signal waveform feature trees. Finally, the waveforms are matched in terms of similarity. To further substantiate the role of the proposed algorithm, imaging experiments and related comparative data for different targets have been completed. The results show that the accuracy of matching processed by the algorithm exceeds 90%, which is improved by about 50% compared with not using the algorithm for the target whose overlapping part accounts for a large proportion of itself.

© 2021 Optical Society of America

Full Article  |  PDF Article
More Like This
Denoising method for a lidar bathymetry system based on a low-rank recovery of non-local data structures

Bin Hu, Yiqiang Zhao, Rui Chen, Qiang Liu, Pinquan Wang, and Qi Zhang
Appl. Opt. 61(1) 69-76 (2022)

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

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

Interpolation linearization predistortion technology for FMCW LiDAR

Honggang Chen, Le Zhao, Leilei Hu, Long Chen, Bo Zhang, Yong Luo, Xuerui Liang, and Linfei Gan
Appl. Opt. 63(6) 1538-1545 (2024)

Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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 (11)

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 (3)

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 (2)

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