Research on 3D range reconstruction algorithm of Gm-APD lidar based on matched filter

The peak-picking method which is commonly used in Gm-APD laser radar 3D reconstruction always gets the wrong target position when there is an abnormal peak, and the reconstructed image has low signal-to-noise ratio and target missing because the threshold can only be integer. To solve these problems, a weighted Gaussian-like matched filtering algorithm was proposed. Fitting the echo firing histogram and normalizing can get the weight. Then the weighted window smoothing histogram was used and the peak position was selected again for reconstruction. According to the Poisson distribution of Gm-APD, the detection probability and false-alarm probability expression of the algorithm can be obtained, then compared with the peak method. The result show that the weighted Gaussian-like matched filter algorithm is better for the target in the middle of the gate. The theoretical derivation results are verified by Monte Carlo simulation. At last, by using the real experimental data and reconstructing data with two kinds of algorithms, the consequence shows that the weighted Gaussian-like matched filtering algorithm has a significant improvement on the restoration subjective and objective compared with the peak method. The results show that this algorithm has a good practical application prospect in dealing with low SNR and real-time 3D reconstruction.