Bayesian learning scheme for sparse DOA estimation based on maximum-a-posteriori of hyperparameters

In this paper, the problem of direction arrival estimation is addressed by employing Bayesian learning technique in sparse domain. This paper deals with inference (SBL) for both single measurement vector (SMV) and multiple (MMV) its applicability to estimate arriving signal’s at receiving antenna array; particularly considered be a uniform linear array. We also derive hyperparameter updating equations maximizing posterior hyperparameters exhibit results nonzero hyperprior scalars. The presented shows that resolution speed proposed algorithm comparatively improved almost zero failure rate minimum mean square error estimate.