A recursive Bayesian beamforming for steering vector uncertainties

Abstract

A recursive Bayesian approach to narrowband beamforming for an uncertain steering vector of interest signal is presented. In this paper, the interference-plus-noise covariance matrix and signal power are assumed to be known. The steering vector is modeled as a complex Gaussian random vector that characterizes the level of steering vector uncertainty. Applying the Bayesian model, a recursive algorithm for minimum mean square error (MMSE) estimation is developed. It can be viewed as a mixture of conditional MMSE estimates weighted by the posterior probability density function of the random steering vector given the observed data. The proposed recursive Bayesian beamformer can make use of the information about the steering vector brought by all the observed data until the current short-term integration window and can estimate the mean and covariance of the steering vector recursively. Numerical simulations show that the proposed beamformer with the known signal power and interference-plus-noise covariance matrix outperforms the linearly constrained minimum variance, subspace projection, and other three Bayesian beamformers. After convergence, it has similar performance to the optimal Max-SINR beamformer with the true steering vector.

A robust adaptive beamforming method based on the matrix reconstruction against a large DOA mismatch

“…Therefore, the robust adaptive beamformer (RAB) has attracted more attention recently. Various RABs have been developed [4,5].…”

Impacts of frequency increment errors on frequency diverse array beampattern

“…Perfect frequency increments are often assumed in existing literatures [20]. However, in an actual array system, there will have imperfect errors including element position errors, mutual coupling, phase errors, and frequency increment errors [21][22][23][24]. Some results have been reported about the impacts of element position error, mutual coupling and phase error on beampattern, and direction-of-arrival (DOA) estimation performances.…”

Recursive Bayesian beamforming with uncertain projected steering vector and strong interferences

“…Evidently, these assumptions are undesirable in practice and greatly limit the applications. In this paper, SIViP we generalize and improve the Bayesian beamforming presented in [14] by considering the unknown signal power and unknown interference-plus-noise covariance matrix. The interferences are assumed to be strong and located far away from the main lobe of the expected beamformer.…”

“…With the inspirations of [11,12], an adaptive beamforming algorithm has been proposed under a recursive Bayesian estimation framework [14], wherein the interference-plusnoise covariance matrix and signal power are assumed to be known. Evidently, these assumptions are undesirable in practice and greatly limit the applications.…”