Underwater source localization based on a matrix distance measurement

Abstract

Abstract In this paper, a matrix distance measurement is introduced to underwater source localization. Matched field processing (MFP) and beamforming(BF) are widely used methods that both make the correlation between data and copy vectors. We proposed statistical matched field processing (SMFP) and statistical beamforming (SBF) in this paper that measure the distance between data and copy cross-spectral density matrices (CSDMs) in place of the correlation of the two vectors. As CSDM is Hermitian and positive semidefinite, Riemann distance is suitable. Matrix-matrix comparison increases the dimension of the system compared with vector-vector correlation, so as to include environmental uncertainty. SMFP and SBF are compared with conventional matched field processing (CMFP) and conventional beamforming (CBF) respectively in simulations. The results show that SMFP and SBF achieve satisfactory localization performances.