A Novel Laser ranging method based on adaptive Kalman filter technology

Abstract

AbstractAiming at the problem of ranging accuracy of laser radar, a laser ranging method based on adaptive Kalman filter is proposed. According to the pulsed laser ranging method and acceleration model based on Wiener process, the standard Kalman filter algorithm is established. In order to solve the problem that the statistical characteristics of laser ranging noise are inconsistent with the actual noise, the autocovariance least squares method is used to estimate the noise parameters. The Monte Carlo method is used to simulate ranging results of laser radar and evaluate the performance of different methods. The adaptive Kalman filtering algorithm using autocovariance least squares method can better adjust the noise parameters and improve the distance measurement accuracy than the traditional Kalman filter algorithm. The experiment of the actual static ranging is carried out. The experiment results show that the SD of laser ranging is reduced from 10.9 to 4.8 mm by using proposed method.