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 i...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 β -norm optimization problem of the compressed Green＇s function and the data received by a vertieal/horizontal line array. The method is validated by simulation and is verified with the experimental data.展开更多
Matched field processing (MFP) is a generalized beamforming method which uses the spatial complexities of acoustic field in an ocean waveguide to localize sources in range, depth and azimuth or to infer parameters of ...Matched field processing (MFP) is a generalized beamforming method which uses the spatial complexities of acoustic field in an ocean waveguide to localize sources in range, depth and azimuth or to infer parameters of the waveguide itself. In the paper, we present simulated and experimental results on narrow-band point source localization in shallow water by the matched field processing of a vertical array. Range-depth ambiguity surfaces are obtained by the spatial correlation of the incident field (modeled or realistic) with a modeled replica of that field. The simulated results indicate that a high-quality ambiguity surface can be obtained in case of perfect match between the 'true' environmental parameters and those used to compute the replica field. The effects of mismatches result in a degraded ambiguity surface and incorrect localization. Examples of localizations obtained with real sea test data are presented. It is shown that the conventional methods have better robustness than the minimum variance distortionless response (MVDR) based method. By employing the reduced minimum variance beamforming (RMVB), we can also get better results.展开更多
基金Supported by the National Natural Science Foundation of China under Grant Nos 11374271 and 11374270the Fundamental Research Funds for the Central Universities under Grant No 201513038
文摘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 β -norm optimization problem of the compressed Green＇s function and the data received by a vertieal/horizontal line array. The method is validated by simulation and is verified with the experimental data.
文摘Matched field processing (MFP) is a generalized beamforming method which uses the spatial complexities of acoustic field in an ocean waveguide to localize sources in range, depth and azimuth or to infer parameters of the waveguide itself. In the paper, we present simulated and experimental results on narrow-band point source localization in shallow water by the matched field processing of a vertical array. Range-depth ambiguity surfaces are obtained by the spatial correlation of the incident field (modeled or realistic) with a modeled replica of that field. The simulated results indicate that a high-quality ambiguity surface can be obtained in case of perfect match between the 'true' environmental parameters and those used to compute the replica field. The effects of mismatches result in a degraded ambiguity surface and incorrect localization. Examples of localizations obtained with real sea test data are presented. It is shown that the conventional methods have better robustness than the minimum variance distortionless response (MVDR) based method. By employing the reduced minimum variance beamforming (RMVB), we can also get better results.
