Compressing the computational burden of matched‐field processing for sound source localization.

Abstract

Sound source localization in shallow water environment is done using matched field processing (MFP), also referred to as time‐reversal imaging or backprojection method. Standard MFP is usually implemented by matching (using a cross‐correlation operation) the received acoustic data from the sought source with modeled data (or replicas) for point source located at multiple test locations over the a priori search area. Consequently, a direct implementation of MFP (i.e., brute force search) over a large search area can be highly computationally demanding especially when attempting to locate repeatedly several stationary or moving unknown sources in a complex environment. We formulated instead a compressive MFP approach allowing for significant computation time savings with a computational cost that scales with the logarithm of the number of independent‐or uncorrelated‐ test locations. This approach leverages the key concepts behind group testing and compressed sensing by computing instead the expected value of multiple ambiguity surface realizations obtained by backprojection of orthogonal random signals from the receiver locations. Thus compressive MFP allows to estimate the actual ambiguity function using few replica computations by evaluating hypothetical random groups of test locations instead of individual test locations (as done in conventional MFP).