A Modified EKF for Vehicle State Estimation With Partial Missing Measurements

During vehicle driving, all aspects of data monitorings are not accurate enough for the vehicle, and there may be packet loss of measurement data. In addition, the accuracy of vehicle data is more difficult to guarantee when the vehicle state is continuously changing, which may lead to some potential safety hazards during driving. Consequently, many algorithms, which only use the statistical characteristics of packet loss information, have been proposed to improve the accuracy. However, with the rapid development of technology, the time-stamp technique in sensor networks can obtain packet loss information at the current moment. In contrast, although the time-stamp technique can effectively improve the filter performance, it cannot analyze the convergence of the Riccati equation. Therefore, this paper proposes a modified EKF algorithm for balancing these two algorithms, and meanwhile, simulation experiments test and verify the effectiveness and feasibility of our proposed algorithm.