Abstract
Measured radar clutter often exhibits a low-rank structure that can be exploited for designing adaptive moving target detectors to reduce the amount of secondary data necessary for desirable detection performance. It has been shown recently that for symmetrically spaced pulse trains significant performance improvements can be achieved by constraining the roots of the Adaptive Matched Filter (AMF) polynomial onto the unit circle (UC) that satisfies a theoretical property of the known-covariance AMF. In this paper, the UC-roots approach is extended to develop a generalized likelihood ratio test (GLRT) for radar moving target detection in low-rank clutter with limited secondary data. The proposed unit circle GLRT (UC-GLRT) uses the properties of orthogonal projection matrices to enforce the UC roots constraint. The asymptotic performance analysis of the proposed UC-GLRT conducted in this paper is verified by simulation. Simulation studies with limited secondary data demonstrate the superior performance of the UC-GLRT approach over several existing detectors. Clutter rank and target velocity mismatch issues are also studied.
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