Evaluation of reduced-rank, adaptive matched field processing algorithms for passive sonar detection in a shallow-water environment

This paper evaluates the performance of several reduced-rank, adaptive matched field processing (AMFP) algorithms for passive sonar detection in a shallow-water environment. Effective rank reduction improves the stability of adaptive beamformer weight calculation when the number of available snapshots is limited. Here, rank-reduction techniques with various criteria for subspace selection are evaluated within a common framework and compared to the full-rank conventional and minimum-variance (MVDR) beamformers. Results from real data demonstrate that rank reduction, properly applied can improve AMFP detection performance in practical system implementations.