This paper develops a complete architecture for constant false alarm rate (CFAR) detection based on a goodness-of-fit (GOF) test. This architecture begins with a logarithmic amplifier, which transforms the background distribution, whether Weibull or lognormal into a location-scale (LS) one, some relevant properties of which are exploited to ensure CFAR. A GOF test is adopted at last to decide whether the samples under test belong to the background or are abnormal given the background and so should be declared to be a target of interest. The performance of this new CFAR scheme is investigated both in homogeneous and multiple interfering targets environment.

A new constant false alarm rate (CFAR) detection scheme based on the goodness-of-fit (GoF) test is proposed to deal with the problem of extended object detection in high resolution synthetic aperture radar (SAR) images. The performance of this detector is compared with that of the traditional detectors using the MSTAR database. Results show that the proposed detector is superior to the traditional detectors in controlling false alarms in nonhomogeneous environment where boundaries widely exist.