High-Precision Depth Image Estimation for Array Gm-APD LiDAR Based on Dual-Parameter Model Feature in Dynamic...

Area of Science:

• Photonics and Optical Engineering
• Remote Sensing Technologies
• Signal Processing for Imaging

Background:

• Array Gm-APD LiDAR performance degrades in scattering environments due to obscurants and backscattering.
• Photon-starved conditions limit depth imaging accuracy in dynamic atmospheric interference.
• Existing methods struggle with effective noise suppression and target integrity in highly scattering scenarios.

Purpose of the Study:

• To develop a depth imaging estimation algorithm for dynamic atmospheric obscurants.
• To enhance depth imaging accuracy by discriminating and suppressing interference.
• To improve target integrity and performance of LiDAR systems in scattering environments.

Main Methods:

• A three-step strategy involving data preprocessing, adaptive interference identification, and temporal correlation-based multi-frame fusion.
• Utilizing continuous multi-frame depth image fusion to leverage temporal correlations.
• Implementing adaptive algorithms to identify and isolate interference-source regions.

Main Results:

• Achieved target recovery rates (TR) from 0.71 to 0.89 and structural similarity (SSIM) from 0.89 to 0.94.
• Reduced root mean square error (RMSE) by at least 23.6% compared to traditional methods.
• Demonstrated significant improvements under various attenuation lengths and occlusion ratios, including a TR of 0.89 under challenging conditions.

Conclusions:

• The proposed algorithm effectively suppresses dynamic noise and improves target integrity in scattering environments.
• The method shows considerable potential for advanced depth imaging applications in challenging atmospheric conditions.
• Significant performance gains in TR, SSIM, and RMSE highlight the algorithm's efficacy over traditional approaches.