Inhomogeneity Suppression CFAR Detection Based on Statistical Modeling

An inhomogeneity suppression constant false alarm rate detector (IS-CFAR) based on statistical modeling is proposed for inhomogeneous sonar or radar data. First, the inhomogeneous background is modeled and classified based on ordered statistics. Then, the background power is estimated based on the different group of data according to the model of the inhomogeneous background. Finally, the IS-CFAR is designed to improve the detection performance for inhomogeneous sonar or radar data. Simulation results show that the IS-CFAR detector can suppress the background inhomogeneity and improve the CFAR detection performance under inhomogeneous background.