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Non-Gaussian clutter modeling and application to radar target detection

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3 Author(s)
Keckler, A.D. ; Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., NY, USA ; Stadelman, D.L. ; Weiner, D.D.

The performance of a Gaussian receiver in non-Gaussian clutter is significantly inferior to that of an optimum receiver for the detection of weak targets. Knowledge of the clutter distribution is a key element to obtaining improved performance. This requires the specification of a suitable multivariate non-Gaussian probability density function (PDF) for the clutter. Spherically invariant random vectors (SIRVs) are used for the characterization of non-Gaussian clutter data. The Ozturk (Rangaswamy et al. 1993; Ozturk and Dudewicz 1992) algorithm, an efficient technique for approximating univariate distributions, is extended to the case of multivariate SIRV distributions. The performance of a novel adaptive non-Gaussian receiver, which utilizes the Ozturk algorithm, is evaluated for a K-distributed clutter example. This receiver shows significant improvement over the Gaussian receiver and closely matches the performance of the K-distributed receiver

Published in:

Radar Conference, 1997., IEEE National

Date of Conference:

13-15 May 1997