Sparse Erroneous Vehicular Trajectory Compression and Recovery via Compressive Sensing

Abstract

Vehicle tracking information is necessary to enable safety communication systems and intelligent transportation systems. Compression technologies with high efficiency and low complexity provide a promising approach to address the transmission and computing problems in vehicle tracking applications. Especially, vehicular trajectory with sparse errors that happened in the measurement sensing process poses a great challenge on traditional compression algorithms. In this paper, we analyze and design a compressive sensing (CS) based erroneous trajectory compression and recovery algorithm for vehicle tracking scenario. Moreover, some theoretical bounds for the proposed recovery optimization problem are analyzed and proved. The CS-based method proposed in this paper could not only achieve a fairly high compression rate and recovery accuracy, but fit the bandwidth mismatch between the road side unit (RSU) and on board unit (OBU). In another aspect, the Kalman filtering (KF) technology is applied for further optimizing the system performance, e.g. mean square error (MSE). Extensive simulations with real vehicular trajectories are carried out, which shows that CS-based compression algorithm achieves relatively high compression performance compared to some state-of-the-art trajectory compression algorithms.