Source-Space Compressive Matched Field Processing for Source Localization

Source localization by matched-field processing (MFP) can be accelerated by building a database of Green's functions which however requires a bulk-storage memory. According to the sparsity of the source locations in the search grids of MFP, compressed sensing inspires an approach to reduce the database by introducing a sensing matrix to compress the database. Compressed sensing is further used to estimate the source locations with higher resolution by solving the l_1-\rm norm optimization problem of the compressed Green's function and the data received by a vertical/horizontal line array. The method is validated by simulation and is verified with the experimental data.