A new adaptive CFAR processor in multiple target situations

In this paper, we deal with the problem of radar target detection in presence of severe interfering targets. To this end, we propose a new Constant False Alarm Rate (CFAR) detection approach based on evaluating the abrupt changes in the background power level samples i.e number of interfering target and/or clutter edge. The proposed new detector is a CFAR detector based on a difference hypothesis of second order statistics, referred as SODCFAR detector. Unlike some recent works that use the information theoretic criteria (ITC) or the variability of the background clutter power level samples, this contribution detects the abrupt changes, analyzes the distribution exponentially of the remaining samples and select the homogeneous window based on the second-order statistics and the Shapiro-Wilk exponentially test. The proposed CFAR detector does not require any prior information about the background environment. Therefore, the performances of the proposed detector combine the advantages of the Order Statistics-CFAR (OS-CFAR) and the Cell Averaging CFAR (CA-CFAR) detectors to immune to the presence of randomness interfering targets among the reference cells. Numerical simulations that validate the performances of the proposed detector, in both homogeneous and nonhomogeneous environments, are presented.