Improved resolution DoA estimation through shrunk projections on the signal sub-space

Abstract

Maximum likelihood (ML) estimation of the Direction of Arrival (DoA) parameters of multiple signals impinging on a sensor array is known to provide best performances among existing techniques, under general signal and system assumptions. However, even the ML estimation performance deteriorates severely in system conditions where the angular separations between signal sources are small and the SNR/sample size are low. In an effort to improve on the ML performance in such challenging conditions, the present communication investigates DoA estimators obtained by performing shrunk (non-orthogonal) projections on the signal sub-space (SS). It is argued that suitable selections of the introduced shrinkage parameters help to limit the chance of outlier estimates occurring, which account for the rapid deterioration of ML at low SNR. Simulation results show that a proposed two-stage estimation approach based on the Shrunk Projections (SP) estimator, offers significant performance gains relative to ML.