Sequential sparse Bayesian learning for beamforming

Abstract

A sequential Bayesian method for beamforming is presented for the estimation of the directions of arrivals (DOAs) of source signals which are varying over time. The sparse Bayesian learning (SBL) uses prior information of unknown source amplitudes as a multi-variate Gaussian with zero-mean and time-varying variance parameters. For sequential processing, we utilize the unknown variance as statistical information across time and propose a sequential SBL-based method to improve time-varying DOA estimation performance. The suggested method has two steps. A prediction step calculates the prior distribution of the current variance parameter from the variance parameter estimated at the previous time step, and an update step incorporates the current measurements. This SBL approach with sequential processing provides high-resolution for time-varying DOA tracking. We evaluate the proposed method using simulated data and real data from the SWellEx-96 experiment.