Depth Imaging Through Smoke Using Nonparametric Estimation for Array Gm-APD LiDAR.

Array Gm-APD LiDAR is highly vulnerable to strong backscattering caused by dynamic smoke. Conventional depth imaging methods cannot rapidly identify the smoke occlusion state, which greatly reduces the target recovery quality of the reconstructed depth image. To solve this problem, this paper presents a non-parametric algorithm for rapid smoke detection and depth imaging for array Gm-APD LiDAR. The proposed method does not rely on parameter estimation of the echo model. Instead, it determines the presence of smoke occlusion by calculating the Pearson correlation coefficient between the echo signal obtained from the superposition of all array pixels and the instrument response function. In this way, the method rapidly identifies smoke interference in a single depth image, performs fast denoising, and reconstructs the depth image. In a dynamic smoke environment with an average attenuation length of no more than 5.1, the proposed algorithm achieves 100% accuracy in occlusion discrimination based on 250 frames of array data. When the smoke occlusion rate reaches 96% and the average attenuation length is 2.29, the method obtains a target recovery of 0.71, which is 86.8% higher than that of the conventional algorithm. These results indicate that the proposed method has strong practical value for array Gm-APD LiDAR, especially for high-speed depth imaging in harsh atmospheric environments with severe obscuration.