A Multi-Sensor Interacted Vehicle-Tracking Algorithm with Time-Varying Observation Error

Abstract

Vehicle tracking in the field of intelligent transportation has received extensive attention in recent years. Multi-sensor-based vehicle tracking system is widely used in some critical environments. However, in the actual scenes, the observation error of each sensor is often different and time varying because of the environmental change and the channel difference. Therefore, in this paper, we propose a multi-sensor interacted vehicle-tracking algorithm with time-varying observation error (MI-TVOE). The algorithm establishes a jointed and time-varying observation error model for each sensor to indicate the variation of observation noise. Then, we develop a multi-sensor interacted vehicle-tracking algorithm which can predict the statistical information of a time-varying observation error and fuse the tracking result of each sensor to provide a global estimation. Simulation results show that the proposed MI-TVOE algorithm can significantly improve the tracking performance compared to the single-sensor-based tracking method, the traditional unscented Kalman filter (UKF), the apdative UKF method (AUKF) and the multi-error fused UKF method (MEF-UKF), which will be well applied to the complex tracking scenes and will reduce the computational complexity with time-varying observation error. The experiments in this paper also prove the superiority of the proposed MI-TVOE algorithm in complex environments.