Abstract
In order to improve the robustness and positioning accuracy of the matched field processing (MFP) in underwater acoustic systems, we propose a conditional probability constraint matched field processing (MFP-CPC) algorithm in this paper, which protects the main-lobe and suppresses the side-lobe to the AMFP by the constraint parameters, such as the posterior probability density of source locations obtained by Bayesian criterion under the assumption of white Gaussian noise. Under such constraint, the proposed MFP-CPC algorithm not only has the same merit of a high resolution as AMFP but also improves the robustness. To evaluate the algorithm, the simulated and experimental data in an uncertain shallow ocean environment is used. From the results, MFP-CPC is robust to the moored source, as well as the moving source. In addition, the localization and tracking performances of using the proposed algorithm are consistent with the trajectory of the moving source.
Highlights
Targets detection and recognition are popular research topics in both civilian and military applications
In order to make a quantitative evaluation for the three kinds of Matched field processing (MFP), we introduce two parameters for performance evaluation, one is signal to interference noise ratio (SINR), the other is the peak to background ratio (PBR)
The mismatch between the environment model and the real environment always leads to a serious performance penalty of MFP, especially in AMFP due to the uncertainty of the prior
Summary
Targets detection and recognition are popular research topics in both civilian and military applications. Other types of environmental mismatch, such as sound velocity profile and geoacoustic parameters mismatch is not similar, in this case, the MV-NLC will fail; MV-EPC protects the main-lobe by using the first and second-order statistical properties of the replica of signal correlation matrix within the scope of environmental parameter perturbation, with heavy computation. We verify the performance of our algorithm by the simulation data of a typical mismatch environment general mismatch model (GENLMIS), which is published in the Naval Research Laboratory seminar in 1993 [19]. Through the processing and analysis with the ocean experimental data from the SACLANT research center in 1993, making a comparison of the Bartlett, MVDR, and MFP-CPC location capability, verifying the validity and robustness of the MFP-CPC algorithm in the uncertain environment. P( m|x) frequency of sound source distance depth sound propagation channel parameter set sound pressure vector amplitude of the complex signal noise vector channel transmission function wave number
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