Track Detection of Underwater Moving Targets Based on CFAR

Abstract

In this paper, we propose a 2D-Weibull-Constant False Alarm Rate (2D-Weibull-CFAR) detection algorithm to solve the problem that detecting current underwater targets is difficult due to the influence of reverberation noise. Specifically, referring to the idea that CFAR uses the probability distribution of reference units to detect objects, this paper introduces the pixel distribution of reverberation noise into the CFAR detector. After that, the probability distribution of the extracted reference units is estimated, and then the adaptive detection threshold is obtained to achieve reliable detection of underwater targets. Finally, the Hough transform extracts the trajectory of the detection results. The experimental test shows that the algorithm in this paper can solve the problem of false alarms and missed alarms in detecting targets hidden in the reverberation noise. The algorithm in this paper can effectively detect the target in the reverberation noise. The detection results show that the algorithm in this paper has higher accuracy and lower false alarm rate than the comparison algorithm.