| Summary: | Based on L-shaped array (LSA), a variety of algorithms for direction of arrival (DOA) estimation have been developed in recent years. However, conventional methods are sensitive to the mutual coupling which may cause the performance of the DOA estimation to degrade dramatically or even fail. In order to solve this problem, a new algorithm for DOA estimation is proposed in this paper. Different from the existing algorithms, the DOA estimation of LSA with mutual coupling is achieved from the perspective of sparse Bayesian learning. A hierarchical form of the Student <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> prior is used to enhance the sparsity of source signal and achieve excellent performance for angle estimation. In the meanwhile, the mutual coupling effect is compensated blindly by sacrificing some sensors as auxiliary components and the hyperparameters and parameters updates are obtained by the expectation-maximization method. Simulation results verify that the performance of our method is superior than that of the state-of-art methods, particularly in the scenario of highly correlated and coherent signals.
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