THEORY OF PARTIALLY ADAPTIVE RADAR

This work extends the recently introduced cross-spectral metric for su bspace selection and dimensionality reduction to partially adaptive sp ace-time sensor array processing, A general methodology is developed f or the analysis of reduced-dimension detection tests with known and un known covariance. It is demonstrated that the cross-spectral metric re sults in a low-dimensional detector which provides nearly optimal perf ormance 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 pr ocess to achieve reduced-dimensional target detection, Finally, it is demonstrated that the cross-spectral subspace reduced-dimension detect or can outperform the full-dimension detector when the noise covarianc e is unknown, closely approximating the performance of the matched fil ter.