Enhanced minimum description length CFAR based on median absolute deviation

Abstract

The actual radar exposure area contains different types of landforms, resulting in non-uniform radar clutter, which significantly reduces the target detection performance and makes it difficult to maintain a constant false alarm probability. This paper proposes an enhanced minimum description length CFAR(EMDL-CFAR) based on median absolute deviation. The algorithm has good target detection performance in the clutter edge environment, in addition, also guarantees the detection performance in the multi-target environment. Using the insensitivity of the median absolute deviation to interference, select different reference samples after the clutter edge detection determines the clutter edge position, and then use the median absolute deviation(MAD) hypothesis test to eliminate the interference from the samples. The performance of the algorithm under different clutter backgrounds is evaluated through simulation, and the superiority of the algorithm is explained.