A new CFAR detector with greatest of selection

This paper presents a new CFAR detector based on ordered statistics (OS) and cell-averaging (CA) forming local estimates, and using greatest-of selection (GO) to form interference power level estimate Z in test cell (OSCAGO). Under the Swerling II assumption, we derive analytic expressions of P_fa, P_d and ADT of this detector, analyze its detection performance in homogeneous background and in strong interfering targets environment, and compare it with OS and GOSGO CFARs. Results show that the detection performance of OSCAGO in homogeneous background and in multiple-target situations is obviously much better than that of OS and GOSGO. When the number of interfering targets is equal to certain value, the CFAR losses of OSCAGO are about 3dB less than that of GOSGO