Vehicle Positioning Utilizing Single-Snapshot DOA and Signal Magnitude-Phase Estimation

Most of existing direction of arrival (DOA) based vehicle positioning techniques are established on array sample covariance matrix and multiple measurement data, which suffer from severe performance degradation in case of a single snapshot. In this paper, a challenging vehicle positioning scheme based on single-snapshot DOA and impinging signal magnitude-phase estimation is proposed. In detail, DOA is initially estimated by applying the generalized approximate message passing combined with belief propagation (GAMP-BP) algorithm under the assumption of complex discrete random variable with distinct phase information. Depending on the initial DOA estimates, two efficient approaches are respectively investigated for final DOA and signal magnitude-phase estimation, where the refine-grid GAMP combined with the least squares algorithm (GAMP-LS) and the special reweighted sparse total least-squares (SRE-STLS) are respectively adopted. With available DOA and magnitude-phase estimates, a principle for selecting reliable DOA sets is further designed, finally enabling improved vehicle positioning without ambiguity under multiple collaborative road side units (RSUs). Simulations are performed to show the effectiveness of the proposed solution.