Quasi best weighted order statistics CFAR detector

In order to make the detector perform robustly, an intuitive solution can be obtained by censoring the clutter observations from large and small deviations. In recent studies, censoring techniques have been implemented using order statistics (OS) and trimmed mean (TM) methods. In order to utilize censored data, we present a quasi best weighted (QBW) order statistics method resulting in an improved detection performance while censoring outliers. Under the assumption of Swerling II target and Rayleigh distributed clutter, the analytic expressions of P_fa, P_d and ADT are derived. The analytic results show that an improved performance over TM is obtained both in homogeneous background and in clutter edge situations, and QBW exhibits robustness in a multiple interfering target environment. In specific cases, QBW reduces to the censored mean level detector (CMLD)