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5G millimeter wave (mmWave) signals can enable accurate positioning invehicular networks when the base station and vehicles are equipped with largeantenna arrays. However, radio-based positioning suffers from multipath signalsgenerated by different types of objects in the physical environment. Multipathcan be turned into a benefit, by building up a radio map (comprising the numberof objects, object type, and object state) and using this map to exploit allavailable signal paths for positioning. We propose a new method for cooperativevehicle positioning and mapping of the radio environment, comprising amultiple-model probability hypothesis density filter and a map fusion routine,which is able to consider different types of objects and different fields ofviews. Simulation results demonstrate the performance of the proposed method.

5G mmWave Cooperative Positioning and Mapping using Multi-Model PHD Filter and Map Fusion