Constant false alarm rate (CFAR) processors are useful for detecting radar targets in background for which all parameters in the statistical distribution are not known and may be nonstationary. The ordered statistics" (OS) CFAR processor based on order statistics has a good performance in homogeneous and nonhomogeneous background. The modified OS CFAR processor, known as "trimmed mean" (TM) CFAR processor performs somewhat better than the OS CFAR processor. But these two CFAR processors can not reduce excessive false alarm rate at clutter edges. In this thesis, we propose and analyze a new CFAR processor called "maximum trimmed mean" (MX-TM) CFAR processor combining the "greatest of" (GO) CFAR and TM CFAR processors. The MAX operation is included to control false alarms at clutter edges. Our analyses show that the proposed CFAR processor has similar performance with the TM CFAR and OS CFAR processors in homogeneous case and multiple target environments, but can control the false alarm rate at clutter edges. It is also show that the MX-TM CFAR processor can reduce the processing time."
