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 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.

Matched Field Localization Based on CS-MUSIC Algorithm

The problem caused by shortness or excessiveness of snapshots and by coherent sources in underwater acoustic positioning is considered.A matched field localization algorithm based on CS-MUSIC(Compressive Sensing Multiple Signal Classification) is proposed based on the sparse mathematical model of the underwater positioning.The signal matrix is calculated through the SVD(Singular Value Decomposition) of the observation matrix.The observation matrix in the sparse mathematical model is replaced by the signal matrix,and a new concise sparse mathematical model is obtained,which means not only the scale of the localization problem but also the noise level is reduced;then the new sparse mathematical model is solved by the CS-MUSIC algorithm which is a combination of CS(Compressive Sensing) method and MUSIC(Multiple Signal Classification) method.The algorithm proposed in this paper can overcome effectively the difficulties caused by correlated sources and shortness of snapshots,and it can also reduce the time complexity and noise level of the localization problem by using the SVD of the observation matrix when the number of snapshots is large,which will be proved in this paper.

An experimental study on matched field processing in shallow water

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.

Geoacoustic inversion for sediments in the South China Sea based on a hybrid inversion scheme

We performed a long range acoustic propagation experiment in the South China Sea(SCS) in November 2004.The environment of the experiment was with an isothermal sound speed profile,where influence of water volume fluctuation was small,meaning that bottom parameters can be well estimated from acoustic signals.We inverted the acoustic parameters of sediment by using a hybrid inversion scheme that combines the matched field processing inversion with Hamilton sediment empirical relationship and transmission loss data.The numerical results show excellent agreement with the experiment data,indicating validity of the inverted parameters.