Paper
19 October 2012 Lidar range profile reconstruction by using chaotic signals and compressive sensing
B. Verdin, R. von Borries
Author Affiliations +
Abstract
Full waveform lidar systems are capable of recording the complete return signal from the laser illuminated target. By making use of the return full waveform, one can obtain more detailed information about the target of interest than the simple target range. The development of better methods to extract information from the return signal can lead to better target characterization. Several methods have been proposed in the literature to obtain the complete range profile or radar cross section of the target.1, 2 In a previous work, we proposed to use a compressive sensing scheme to acquire and compress the received signal, and at a post-processing stage reconstruct the signal to obtain the range profile of the target. We extend this previous work on full waveform lidar using chaotic signal by including additive white Gaussian noise into the acquisition stage of the lidar system. The objective is to test the robustness of the previously developed approach based on compressive sensing to different noise level intensities. The simulation software Digital Imaging and Remote Sensing Image Generation (DIRSIG) was used to simulate the range profile corresponding to a three-dimensional scene. The simulation results indicate that the full range profile can be reconstructed with a compressive sensing acquisition as low as 25 percent of the total number of samples and with low root-mean-square error (RMSE). The proposed lidar system with compressive sensing can be used to sense with compression and recover the range target profile.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
B. Verdin and R. von Borries "Lidar range profile reconstruction by using chaotic signals and compressive sensing", Proc. SPIE 8512, Infrared Sensors, Devices, and Applications II, 85120W (19 October 2012); https://doi.org/10.1117/12.928998
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KEYWORDS
LIDAR

Compressed sensing

Semiconductor lasers

Signal to noise ratio

Interference (communication)

3D image enhancement

Sensing systems

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