5G mmWave Cooperative Positioning and Mapping using Multi-Model PHD Filter and Map Fusion
5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station (BS) and vehicles are equipped with large antenna arrays. However, radio-based positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. Building such a map is challenging, due to the inherent data association uncertainty, missed detection, and clutter. We propose a new method for cooperative vehicle positioning and mapping of the radio environment in order to address these challenges. The proposed method comprises a multi-model probability hypothesis density (PHD) filter and a map fusion routine, which is able to consider different types of objects and different fields of views (FoVs). Simulation results demonstrate that the proposed method handles the aforementioned challenges, and improves the vehicle positioning and mapping performance.
PDF Abstract