A robust adaptive weighted CFAR detector based on truncated statistics

Abstract

Constant false alarm rate (CFAR) detectors are widely used in modern radar system to declare the presence of targets. One or more outliers will appear in the reference cell under the multiple strong interferences situation, and the clutter power estimation will increase, which will affect the detection threshold calculation, the detection probability of CFAR detectors decrease and the alarm rates increase significantly. This paper proposes an adaptive weighted truncation statistic CFAR (AWTS-CFAR) algorithm and achieves good performance. By improving the truncation process, the truncated larger value is adaptively weighted with the smaller value in the reference cell. Since AWTS-CFAR makes the larger value in the reference cell also participate in the calculation of the background clutter power estimation, even if the truncation threshold is selected to be smaller, AWTS-CFAR will not cause too much loss of constant false alarm, and will suppress clutter edge effect as much as possible in the clutter edge environment.