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Abstract

Source localization in an ocean waveguide is a challenging problem because of the complexity of underwater acoustic propagation. Matched-field processing (MFP) has attracted considerable attention and has become a crucial technique for underwater acoustic source localization. Compressive sensing can achieve spatial sparsity, thus improving spatial resolution, by imposing penalties based on l1-norm. In this study, we developed a robust super-resolution approach for source localization in an ocean waveguide, which utilizes the inherent sparse structure of the spatial localization problem and underwater sound propagation principle. The proposed approach can be formulated as a sparse representation problem and further converted into a convex optimization problem with sparsity constraints. Moreover, the approach can be easily implemented and efficiently solved using currently available convex optimization software toolboxes based on interior point algorithms, such as CVX. The approach can also be extended to multiple-measurement scenarios for achieving superior source localization performance. In addition, the effect of the signal-to-noise ratio (SNR), a constraint parameter, and model mismatch on source localization performance was thoroughly analyzed using computer simulations. Numerical simulation results demonstrated that in some challenging scenarios, the proposed approach exhibited superior performance compared with existing conventional methods, such as a low source localization error and high mainlobe-to-sidelobe ratio.

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