Vehicle Behavior-Cognition-Based Particle-Filter-Enabled mmWave Beam Tracking for Connected Automated Vehicles

Abstract

Considering the low-latency and high data rate requirements for automated vehicles (AVs), the millimeter-wave (mmWave) technology can support tens of Gb/s raw sensor information sharing for connected AVs (CAVs). However, the challenging problem is how to achieve fast and robust mmWave beam tracking for CAVs. To solve this problem, we propose a novel vehicle behavior cognition-based particle-filter (VBC-PF)-enabled beam tracking algorithm. The beam-space subset is predicted based on the beam change rate and the position-yaw information from the vehicle behavior cognition in CAVs, which effectively reduces the beam search overhead. In the proposed VBC-PF algorithm, the particle weight updating schemes are designed based on the optimal vehicle behavior cognition to avoid the particle divergence and the error accumulation. Simulation and hardware testbed results verify that the accuracy and efficiency of the proposed VBC-PF algorithm outperform the conventional particle filter (PF) and the extended Kalman filter (EKF) algorithms.