Time-correlated Kalman Depth Estimation of Photon-counting Lidar

In the lidar long-distance imaging scene with high background noise and low integration time,in view of the problem of the depth image target obtained by traditional methods being submerged by noise and the large deviation of depth estimation,a method based on signal photon time correlation and adaptive Kalman filter depth information estimation method is proposed. The photon counts with aggregation feature in time will be extracted to form a set firstly;then the factors that affect the temporal distribution of signal photons will be analyzed and Gaussian linear model will be used to described the photon set;finally,the time-of-flight of all photons in the set will be scrambled and input into the improved adaptive Kalman filter to iteratively estimate the depth value. In the room with signal to noise ratio of 1, compared with the traditional maximum likelihood method,this method improves the root mean square error by 40% and 38% when the integration time is 10 ms and 1ms respectively. In the outdoor 2 km target imaging experiment with signal to noise ratio of about 0.135, when the signal photon numbers are 100,33 and 17 respectively,the depth image of this method is clearer and the noise is lower than the traditional maximum likelihood method and fast denoising algorithm with the temporal correlation of photons. It is verified that the method can be applied to the depth information estimation and image restoration of lidar remote imaging under high noise and short integration time.