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Proceedings Paper

Robust CFAR detection using order statistic processors for Weibull-distributed clutter
Author(s): Daniel T. Nagle; Jafar Saniie
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Paper 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.

Paper Details

Date Published: 1 August 1991
PDF: 15 pages
Proc. SPIE 1481, Signal and Data Processing of Small Targets 1991, (1 August 1991); doi: 10.1117/12.45644
Show Author Affiliations
Daniel T. Nagle, Illinois Institute of Technology (United States)
Jafar Saniie, Illinois Institute of Technology (United States)


Published in SPIE Proceedings Vol. 1481:
Signal and Data Processing of Small Targets 1991
Oliver E. Drummond, Editor(s)

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