Adaptive Surface Interference Suppression for Matched-Mode Source Localization

Abstract

Localizing a quiet submerged target in the presence of loud interfering surface ships is an important problem for matched-field processing (MFP) in shallow water. Typically, a data-driven interference suppression scheme is employed which requires neither prior information of the interferer's location nor filter design optimization and iterative estimation. However, the target and the interferers are usually in motion resulting in spreading or mixing of signal energies in their subspaces, thus making it difficult to determine the interference subspace dimension. In this paper, we exploit the difference in modal amplitudes for surface and submerged sources by eigenanalysis of the modal cross-spectral density matrix (CSDM). Simulation and experimental data results show that the interference subspace can be estimated adaptively and the beam output for the target is enhanced.