Weighted likelihood CFAR detection for Weibull background

In modern radar detection systems, constant false alarm rate (CFAR) control is a key technique for automatic target detection in clutter level unknown environments. The maximum likelihood criterion is often used to design the CFAR detector for which it has the best detection performance with the expected probability of false alarm. However, the heterogeneous environments, viz., multiple-target scenario and clutter power transition, will deteriorate the performance of the optimal CFAR detector. In this paper, we propose a novel CFAR detector for Weibull background with known shape parameter. The new CFAR detector is based on robust weighted likelihood estimator, with robustness to interferences. In addition, to prove the CFAR property of it, we introduce the invariant theory. Computation analysis and simulation results verify the effectiveness and superiority of our proposed CFAR detector.