A Congestion Efficient Cooperative Positioning scheme for vehicular networks

Cooperative positioning approaches based on sharing ranges and kinematics information among clustered vehicles are introduced to comply with the expectations of intelligent transportation systems. However, frequently exchanging data among participating vehicles imposes communication overhead on the network. As the number of vehicles in the cluster increases, the communication overhead may turn into the congestion situation which yields to an unreliability of packets transmission and the throughput degradation. To tackle the above flaws, we propose an initiative Congestion Efficient Cooperative Positioning (CE-CP) scheme which mitigates the probable congestion along with providing a high positioning accuracy in high-density vehicular networks. The main idea is to omit some of the range measurements information at the transmission stage and then recover those omitted measurements at the receiver vehicle using the matrix completion algorithm. To achieve a high accuracy in vehicles' positioning, we incorporate an Unscented Kalman Filter (UKF) to fuse the shared ranges and kinematics information. Simulation results clarify the efficiency of our UKF-based CE-CP scheme in mitigating the congestion phenomenon and attaining an approximately 17% improvement in the positioning accuracy comparing to the extended Kalman filter-based CP approach.