STAP Detection with
Sub-Gaussian Distributions and Fractional Lower-Order Statistics for
Airborne Radar

George A. Tsihrintzis and Chrysostomos L. Nikias
University of Virginia
Department of Electrical Engineering
Thornton Hall
Charlottesville, VA 22903-2442
tel: (804) 924-6146
fax: (804) 924-8818

University of Southern California
Department of Electrical Engineering
Los Angeles, CA 90089-2564

Abstract We address the problem of coherent detection of a signal embedded in heavy-tailed noise modeled as a sub-Gaussian, alpha-stable process. We assume that the signal is a complex-valued vector of length L, known only within a multiplicative constant. The dependence structure of the noise, i.e., the underlying matrix of the sub-Gaussian process, is not known. The intent is to implement a generalized likelihood ratio detector which employs robust estimates of the unknown noise underlying matrix and the unknown signal strength. The performance of the proposed adaptive detector is compared to that of an adaptive matched filter that uses Gaussian estimates of the noise underlying matrix and the signal strength and is found to be clearly superior. The proposed new algorithms are theoretically analyzed and illustrated in a Monte-Carlo simulation.


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