Adaptive CFAR detection in heterogeneous Compound-Gaussian

In this paper, we focus on the performance improvement of constant false alarm rate (CFAR) detector in heterogeneous Compound-Gaussian background. This paper is motivated by the fact that the detectors´ performance degradation when an unknown located clutter edge exists in the reference window that divide the data samples into two different independent and identically distributed (IID) Compound-Gaussian distribution. To account for this issue, we propose an automatic clutter edge estimation algorithm based on goodness of fit (GoF) which can select IID data with the cell under test (CUT), and we also suggest a CFAR detector (CFARD) uses this clutter edge estimation algorithm as preprocessing to enhance the detection performance around clutter edges. Simulations are provided to demonstrate the performance of the proposed CFARD in comparison with Ordered-Statistic-CFARD (OS-CFARD).