The USC Andrew and Erna Viterbi School of Engineering USC Signal and Image Processing Institute USC Ming Hsieh Department of Electrical Engineering University of Southern California

Technical Report USC-SIPI-343

“Theory of Partially Adaptive Radar”

by J. Scott Goldstein and Irving S. Reed

April 1996

This work extends the recently introduced cross-spectral metric for subspace selection and dimensionally reduction to partially adaptive space-time sensor array processing. A general methodology is developed for the analysis of reduced-dimension detection tests with known and unknown covariance. It is demonstrated that the cross-spectral metric results in a low-dimensional detector, which provide nearly optimal performance when the noise covariance is known. It is also shown that this metric allows the dimensionality of the detector to be reduced below the dimension of the noise subspace eigenstructure without significant loss. This attribute provides robustness in the subspace selection process to achieve reduced-dimensional target detection. Finally, it is demonstrated that the cross-spectral subspace reduced-dimension detector can outperform the full-dimension detector when the noise covariance is unknown, closely approximating the performance of the matched filter.

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