Direction-of-Arrival Estimation with Multiple-Dictionary Joint Sparse Representation via Wideband Covariance Matrix

In order to estimate coherent wideband signals directly and achieve a higher resolution, a new method based on wideband covariance matrix using multiple-dictionary joint sparse representation is proposed in the light of DOA (Direction-of-Arrival) estimation of wideband signals. Firstly, the covariance of wideband signals at every discrete frequency point is represented by its overcomplete dictionary, and then the multiple-dictionary joint sparse MMV (multiple-measurement vector) model is obtained. Finally, the DOAs are estimated by solving the multiple-dictionary joint sparse inverse problem with the joint-sparse constraint of MMV’s sparse representation coefficients. For ULA (Uniform Linear Array) structure, the joint sparsity of multiple-dictionary joint sparse MMV model makes this proposed approach can breakthrough the classical spatial sampling theorem, so we can increase the element spacing exceeding half-wavelength spacing which leads to a significant improvement in the resolution limit without spatial ambiguity or aliasing. Noise suppression via the pre-estimation of the noise power can also improve the robustness to DOA estimation in a lower SNR (Signal-to-Noise Ratio). In addition, the proposed method has the capability of estimating both uncorrelated and coherent wideband signals because of its independence with the rank of wideband covariance matrix. The simulation results demonstrated the efficacy of our proposed approach.