Cooperative localisation for multi-RSU vehicular networks based on predictive beamforming

Abstract

The integration of sensing and communication has become essential to next-generation vehicular networks. In this paper, we investigate a vehicle-to-infrastructure (V2I) network with multiple roadside units (RSUs) based on the dual-functional radar-communication (DFRC) technique. Since there are multiple RSUs in the system, we first propose a signal-switching model between vehicles and different RSUs. These RSUs estimate and predict vehicles’ motion parameters based on the DFRC signal echoes and the state evolution model. Accordingly, we utilise a neural network to extract angle information from signal echoes instead of traditional methods, thus improving the angle estimation accuracy. To further improve the estimation performance, we formulate an optimisation problem to minimise the Cramer-Rao bound (CRB) on angle estimation by properly allocating power to each RSU. Finally, we propose a novel weighting method to further improve the cooperative localisation accuracy of the multi-RSU system. Simulation results show that the performance of angle estimation can be improved by utilising the proposed neural network method and the novel power allocation scheme. In addition, the novel weighting method can considerably improve the localisation accuracy.