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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-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.
Source localization by matched-field processing (MFP) can be accelerated by building a database of Green’s functions which 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-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.