Single and multiple snapshot compressive matched field processing

Abstract

Matched field processing is a generalized beamforming method which matches received array data to a dictionary of replicas to locate and track a source. The solution set generally is sparse since there are considerably fewer sources than replicas. This underdetermined problem can be solved with sparse processing (SP) which potentially is attractive for several reasons. The traditional spatial matched-filter problem is reformulated as a convex optimization problem subject to a sparsity constraint. For example, an elastic net seems to be an appropriate penalty in order to find the best match among a correlated group of replicas. Another advantage is that SP does not require inversion of the sample covariance matrix and therefore can outperform conventional high-resolution processors in snapshot deficient scenarios (i.e., fast moving sources). A third potential advantage is that SP can achieve super-resolution at high SNR and discriminate between closely spaced sources. Here, we demonstrate the performance of single and multi-snapshot SP to track a towed source using the SWellEx-96 data set. Results are benchmarked using the Bartlett and the white noise constraint processors. We further discuss the processing of multiple frequencies in order to improve the source tracking.