Compressive matched-field processing

Abstract

Source localization by matched-field processing (MFP) generally involves solving a number of computationally intensive partial differential equations. This paper introduces a technique that mitigates this computational workload by "compressing" these computations. Drawing on key concepts from the recently developed field of compressed sensing, it shows how a low-dimensional proxy for the Green's function can be constructed by backpropagating a small set of random receiver vectors. Then the source can be located by performing a number of "short" correlations between this proxy and the projection of the recorded acoustic data in the compressed space. Numerical experiments in a Pekeris ocean waveguide are presented that demonstrate that this compressed version of MFP is as effective as traditional MFP even when the compression is significant. The results are particularly promising in the broadband regime where using as few as two random backpropagations per frequency performs almost as well as the traditional broadband MFP but with the added benefit of generic applicability. That is, the computationally intensive backpropagations may be computed offline independently from the received signals, and may be reused to locate any source within the search grid area.

Block-sparse beamforming for spatially extended sources in a Bayesian formulation

“…(18)]. Even though we limit this study to recovering spatial source distributions characterized by block-sparsity through such piecewise constant fit, the WL framework accommodates to solutions fitting a more general polynomial trend, e.g., smooth spatial source distributions without discontinuities at the boundaries.…”

“…17 The inclusion of an ' 2 -norm regularizer allows spatially extended sources to appear in the DOA map, that otherwise would be ignored by the ' 1 -norm penalty alone. 18 In a probabilistic formulation, regularization is imposed in the form of prior information on the model parameters. Bayesian estimation theory provides a systematic way to include prior constraints to the data fitting term.…”

Dispersion curve recovery with orthogonal matching pursuit

“…Sparse representations have been used in various applications, including acoustic localization. 6,7 Basis pursuit denoising has been widely studied and is currently known to be one of the best methods, in terms of recovery performance, for extracting sparse representations. Basis pursuit denoising minimizes the squared error between the measured data x q and the model U(d)D j v q with regularization to encourage sparsity in v q .…”

Matched Field Processing Based on Bayesian Estimation

“…is used to build a replica vector of the field in the observation sea area [6], and then match the measured field with the replica to estimate the location of the source or the channel information [7,8]. MFP is mainly divided into two categories: One is the conventional matching field processor (conventional MFP, CMFP [9]), also known as the Bartlett processor; another is adaptive matched field processor (adaptive MFP, AMFP), the representative is the minimum variance distortionless response (MVDR) processor [10,11]. Bartlett is robustness, but it is difficult to separate the main-lobe from side-lobe for its side-lobe is much higher.…”