Vehicle Positioning With Unitary Approximate Message Passing Based DOA Estimation Under Exact Spatial Geometry
Publication Name
IEEE Internet of Things Journal
Abstract
Attaining centimeter-level vehicle positioning is a fundamental requirement for lane-level autonomous driving. Pursing this objective from the perspective of direction-of-arrival (DOA) estimation is promising, which has emerged as a prominent research topic. In order to simultaneously meet the demands of low complexity and high accuracy, it is crucial for DOA-based solutions to address pressing challenges, including the model mismatch problem and reliable positioning in scenarios with limited samples. This paper explores a novel vehicle positioning scheme employing DOAs obtained from collaborative base stations (BSs) or road side units (RSUs). To cope with the actual propagation scenarios and avoid non-random systematic error, the exact spatial geometry (ESG) for DOA estimation is adopted. Under the ESG model, a two-stage unitary approximate message passing (UAMP) based DOA estimation method is proposed. With DOAs estimated at multiple collaborative BSs/RSUs, the locations of vehicles are finally obtained with cross-localization criterion. Numerical simulations are provided to show that the proposed method is effective and delivers competitive performance. Furthermore, inspired by intriguing simulation results, we design a DOA subset selection mechanism that enhances the reliability of positioning performance.
Open Access Status
This publication is not available as open access