Dual-parameter estimation algorithm for Gm-APD Lidar depth imaging through smoke - Zhang, Yinbo; Li, Sining; Sun, Jianfeng; Liu, Di; Zhang, Xin; Yang, Xianhui; Zhou, Xin (Measurement : journal of the International Measurement Confederation, 2022)
•The strong backscattering of smoke limits the adaptability of Gm-APD Lidar for depth imaging.•The smoke model based on Gamma distribution explains the process of photon transmission.•Aiming at the characteristics of small number of Bins and large Bin width, this algorithm uses continuous wavelet transform to extract scale parameter and maximum likelihood method to extract shape parameter.•Dual-parameter estimation algorithm based on Gamma function has more advantages in extreme smoke environment than the traditional algorithms.•Improve the weather adaptability of Gm-APD Lidar. The strong backscattering of smoke limits the adaptability of Gm-APD Lidar for depth imaging through dense smoke. In this paper, a dual-parameter estimation algorithm based on Gamma function is proposed. Aiming at the characteristics of small number of Bins and large Bin width, this algorithm uses continuous wavelet transform to extract scale parameter and maximum likelihood method to extract shape parameter. Based on the estimated two parameters, this study helps to distinguish between background photons reflected from the smoke and target signal photons. The experimental results show that when the smoke density is high or the acquisition time is 0.15 s, the reconstructed object shape is more complete. The distance error of short-range (30 cm) and long-range (75 cm) targets is 1 and 0 Bin respectively, which is at least 6 Bins less than the traditional algorithms. Our algorithm improves the weather adaptability of Gm-APD Lidar.