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 well known "cell averaging"(CA) CFAR processor is known to yield best performance in homogeneous case, but exhibits severe performance degradation in the presence of an interfering target in the reference window or in regions of abrupt change in the background clutter power.
Rohling proposed an "ordered statistics" (OS) CFAR processor based on median filtering. The OS CFAR is known to have good performance above two nonhomogeneous case. The modified OS-CFAR processor, known as the "trimmed mean"(TM) CFAR processor which utilizes trimmed averaging after ordering performs somewhat better than the OS-CFAR processor by judiciously trimming the ordered samples.
In this thesis, TM-CFAR processor is analyzed in homogeneous and nonhomogeneous case. And a new CFAR processor that utilizes "trimmed mean cell averaging" (TMCA) concept is proposed and analyzed. TMCA-CFAR processor has similar performance with the TM-CFAR processor, but reduces computing time by more than one half.
