Numerical Simulation of Performance Improvement of Coherent LiDAR Based on SPGD Algorithm

Atmospheric turbulence can significantly impact the effectiveness of light detection and ranging (LiDAR) in long-range detection. A technique for wavefront correction, which is based on the stochastic parallel gradient descent (SPGD) optimization algorithm, is proposed. The method integrates coherent detection theory with adaptive optics technology, effectively mitigating the adverse effects of turbulence. This work evaluates the suitability of the algorithm in coherent LiDAR through theoretical analysis and establishes the necessary theoretical relationships. Through numerical simulation, we assess its optimization ability for Strehl ratio (SR), bit error rate (BER), signal to noise ratio (SNR), and detection distance (DR). We also conduct a comprehensive analysis of the impact of the number of iterations of the algorithm affecting SR, SNR, and DR. This analysis provides robust data support for balancing the performance of the system. The results show that the corrected SR can reach 0.96, 0.88, and 0.75, the SNR can be improved by 7 dB, 16 dB, and 26 dB, and the DR can be improved by 8%, 17%, and 30% in gentle, moderate, and strong turbulence, respectively.