Optimal and adaptive reduced-rank STAP

This paper is concerned with issues and techniques associated with the deve lopment of both optimal and adaptive (data dependent) reduced-rank signal p rocessing architectures. Adaptive algorithms for 1D beamforming, 2D space-t ime adaptive processing (STAP), and 3D STAP for joint hot and cold clutter mitigation are surveyed. The following concepts are then introduced for the first time (other than workshop and conference records) and evaluated in a signal-dependent versus signal independent context: 1) the adaptive proces sing "region-of-convergence" as a function of sample support and rank, 2) a new variant of the cross-spectral metric (CSM) that retains dominant mode estimation in the direct-form processor (DFP) structure, and 3) the robustn ess of the proposed methods to the subspace "leakage" problem arising in ma ny real-world applications. A comprehensive performance comparison is condu cted both analytically and via Monte Carlo simulation which clearly demonst rates the superior theoretical compression performance of signal-dependent rank-reduction, its broader region-of-convergence, and its inherent robustn ess to subspace leakage.