Adaptive Suppression Method of LiDAR Background Noise Based on Threshold Detection

Abstract

Background radiation in the LiDAR detection field of view is complex and variable, and the background noise generated can easily cause false alarms in the receiver, which affects the effective detection of the system. Through the analysis of the influence of background radiation noise of LiDAR on the detection performance, an adaptive suppression method of LiDAR background noise is proposed. This method realizes the rapid suppression of background noise in the instantaneous field of view through an adaptive threshold adjustment of current steering architecture with a back-end digital-to-analog converter (DAC) correction based on the principle of constant false alarm rate (CFAR) control. Aiming at the problem of accurate noise detection and quantification in a very short time, a dynamic comparator is used to replace the traditional continuous comparator. While detecting the number of noise pulses, the measurement of the pulse duration of noise is realized, which improves the accuracy of short-time noise detection. In order to verify the actual effect of the adaptive method, experiments were carried out based on the team’s self-developed LiDAR. The experimental results show that the measured noise ratio of the adaptive mode by using this method is the lowest. Even at 12 a.m., the noise ratio of the point cloud obtained by the adaptive mode is 0.012%, compared with 0.08% obtained by the traditional mode, which proves that this method has a good ability to suppress background noise. The proportion of noise reduction of the adaptive mode is more than 80% compared with the traditional mode. It achieves noise suppression through hardware at each detection, and each adjustment can be completed within a single period of pulse detection. Therefore, it has great advantages in real-time detection compared with the back-end software noise reduction processing method, and it is suitable for the application of LiDAR in the complex background environment.