Particle-Velocity-Field Difference Smoothing for Coherent Source Localization in Spatially Nonuniform Noise

This communication considers the problem of estimating 2-D directions of arrival (DOAs) of multiple coherent signals under spatially nonuniform noise (spatially inhomogeneous temporary white noise) using an array of vector hydrophones. A novel preprocessing method called particle-velocity-field difference smoothing (PVFDS) is proposed. The key idea underlying the PVFDS is to remove the spatially nonuniform noise by using the matrix difference of pairs of particle-velocity data correlation matrices, and to decorrelate the coherent signals by summing these difference correlation matrices. Unlike most of other existing preprocessing techniques, such as spatial smoothing and forward–backward averaging, the PVFDS processing does not decrease the array aperture. For arbitrary array geometries, the PVFDS can resolve up to four coherent signals, and for centro–symmetric arrays, forward–backward averaging can double this number to eight. Monte Carlo simulations illustrate that the PVFDS-based eigenstructure algorithms can offer better performance than the particle-velocity-field smoothing (PVFS)-based counterparts.