Computationally Efficient Wide-Band DOA Estimation Methods Based on Sparse Bayesian Framework

For practical wide-band direction-of-arrival (DOA) estimation problems, the sparse Bayesian algorithms, implemented by expectation maximization or variational Bayesian inference techniques, generally require prohibitive computational complexity. In this paper, computationally efficient wide-band DOA estimation is considered within a sparse Bayesian framework. In particular, two computationally efficient wide-band DOA estimation methods are proposed, i.e., single and multiple observations sparse Bayesian multitask learning methods, denoted as SO-SBMTL and MO-SBMTL. The proposed methods can independently and jointly process the signals to obtain the DOA estimates, respectively, with significantly reduced computational complexity. In addition, the off-the-grid problem in wide-band DOA estimation is also considered within this framework. The remarkable feature of our proposed methods is that the off-the-grid parameter can be desirably obtained with a closed-form solution, so that numerical searching procedure is appropriately avoided and the computational complexity is substantially reduced. Experimental results have demonstrated that the proposed algorithms can achieve desirable performance with substantially reduced computational complexities. The MO-SBMTL achieves better performance than the SO-SBMTL in the scenarios of static or slow time-varying moving sources, and the off-the-grid algorithm can also efficiently estimate the true DOAs of the sources.