Belief Propagation for Near-Field Cooperative Localization and Tracking in 6G Vehicular Networks

Thanks to the ultra high-speed and low-latency wireless links provided by 5G and beyond networks, vehicular-to-vehicle communications has been brought into reality. Deploying large-scale antenna arrays at higher frequencies is expected to be one of the key-enabling technologies for accurate localization and tracking in vehicular networks. However, different from conventional vehicular systems, the near-field region becomes non-negligible due to the enlarged antenna aperture and reduced wavelength, which has to be taken into account in 6G vehicular networks. This paper investigates the near-field cooperative localization and tracking problem, where the diversity in the angle-of-arrivals at different antenna elements is exploited for location estimation. An algorithm based on belief propagation (BP) is developed, while particle processing with a novel agent selection strategy is proposed to facilitate low-complexity and high-precision localization and tracking. Simulation results will show the effectiveness of the proposed BP algorithm over the state-of-the-art approaches in terms of estimation accuracy.