Wideband DOA Estimation via Joint Sparse Representation of Array Covariance Vectors

Under the frequency-decomposed wideband signal model, a group-sparsity (GS) based wideband sparse representation of array covariance vectors (GS-WSRACV) framework is proposed by fully exploiting information from multiple subbands. Firstly, an unweighted GS-WSRACV method is presented, for which a hyperparameter-tuning process is required. By prewhitening the residual constraint term in the optimization problem of GS-WSRACV (unweighted), a modified GS-WSRACV method is then developed, with a closed-form expression of the hyperparameter derived. Compared to existing prior-information-free sparse reconstruction based DOA estimation methods, the proposed GS-WSRACV (unweighted) achieves the better estimation performance with a reduced computational complexity in each optimization; however, a hyperparameter-tuning process is required and the cost function for hyperparameter selection via trial-and-error is impractical due to the unknown actual DOAs for assessment, and the hyperparameter differs significantly with respect to the input SNR (received source signal powers are also unknown). As a result, the proposed modified GS-WSRACV has achieved the best performance, and it is a more effective solution in practice without time-consuming hyperparameter-tuning process.