Compressive matched field processing.

Abstract

Sound source localization in shallow water environment is commonly done using matched field processing (MFP). MFP is usually implemented by systematically placing a test point source at each point of a search grid, computing the acoustic field (replicas) at all the elements of the array and then correlating this modeled field with the data from the real point source whose localization is unknown to determine the best-fit location. However, a direct implementation of MFP (i.e., brute force search) over a large grid space—or search area—is computationally demanding especially in the presence of complex propagation environments. We formulated instead the localization problem of a few acoustic sources as the sparse approximation of the measured signals in a specific dictionary of atoms obtained from discretization of this sparse search space. Spatial random projections are performed to efficiently span the search space. This compressive MFP approach allows for significant computation time savings with a computational cost increasing with the number of random projections instead of the number of search grid points. The performance of this approach will be illustrated using numerical and experimental data in a shallow water waveguide.