Robust CFAR detection using order statistic processors for Weibull-distributed clutter

Daniel T. Nagle; Jafar Saniie

doi:10.1117/12.45644

1 August 1991 Robust CFAR detection using order statistic processors for Weibull-distributed clutter

Daniel T. Nagle, Jafar Saniie

Proceedings Volume 1481, Signal and Data Processing of Small Targets 1991; (1991) https://doi.org/10.1117/12.45644
Event: Orlando '91, 1991, Orlando, FL, United States

Abstract

CFAR detectors have been utilized in radar systems where the clutter environment is partially unknown and/or has varying statistical properties (e.g., power). In such instances, the performance of the optimal detector deteriorates significantly, and a nonparametric or constant false alarm rate (CFAR) detector is designed to be insensitive to changes in the density functions of the clutter. An effective method of accomplishing this is to use local estimates for the threshold corresponding to the unknown (or varying) parameters of the clutter distribution. Recently, order statistic (OS) processors have been shown to perform robustly (in terms of CFAR loss) for inhomogeneous clutter observations, although these processors utilize order statistics in an inefficient manner in terms of estimating the power of the clutter distribution. For a more efficient threshold estimate and, consequently, more robust detector, the censored maximum likelihood (CML) and best linear unbiased (BLU) estimates are applied toward CFAR detection. In particular, these methods are utilized for estimation of the scale parameter of the Weibull-distributed clutter with known shape parameter. The design of these CFAR detectors and the probability of detection performance under Lehmann's alternative hypothesis are mathematically analyzed.

Citation Download Citation

Daniel T. Nagle and Jafar Saniie "Robust CFAR detection using order statistic processors for Weibull-distributed clutter", Proc. SPIE 1481, Signal and Data Processing of Small Targets 1991, (1 August 1991); https://doi.org/10.1117/12.45644

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