Sound source localization with sensor arrays involves the estimation of the direction-of-arrival (DOA) from a limited number of observations. Compressive sensing (CS) is a method for solving such underdetermined problems, which achieves simultaneously sparsity, thus super-resolution, and computational speed. We formulate the DOA estimation problem in the CS framework and show that CS has superior performance compared to traditional DOA estimation methods. A bias and resolution analysis is performed to indicate the limitations of CS. We show that the bias is related to the beampattern, thus can be predicted. To demonstrate the super-resolution capabilities and the robustness of CS, the method is applied to experimental data from ocean acoustic measurements for source tracking with single-snapshot data.
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