Abstract
In this paper, adaptive signal detection is addressed for range-spread targets in unknown zero-mean Gaussian clutter with persymmetric covariance matrix. The range-spread target can be expressed as the product of a known subspace and its unknown arbitrary coordinates. Under the above assumptions, a persymmetric subspace detector is devised by utilizing the generalized likelihood ratio test (GLRT). Moreover, the proposed persymmetric subspace GLRT-based detector is theoretically proved to be constant false alarm rate to the unknown clutter covariance matrix. Finally, the numerical results demonstrate the effectiveness of the proposed detector, compared with the existing unstructured competitors and persymmetric ones, especially in the limited training data scenarios.
Published Version
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