Research on Intelligent Vehicle Target State Tracking Based on Robust Adaptive SCKF

doi:10.3901/JME.2021.20.181

Abstract

Abstract: Accurate estimation of the states of vehicle and front vehicle is the premise of the effective decision-making and control of the intelligent vehicle. However, previous studies usually do not consider the uncertainty of noise statistical characteristics, which leads to a large error of vehicle state estimation in some cases. Therefore, a robust adaptive square-root cubature Kalman filter (RASCKF) algorithm is proposed to reduce the influence of noise statistical uncertainty on estimation accuracy. Firstly, the statistical values of process noise covariance and measurement noise covariance are estimated by the maximum a posterior (MAP) criterion to improve the accuracy of state estimation when the noise is stable. Secondly, the fault detection rules are designed based on the standardized measurement innovation sequence, and the real-time measurement innovation is used to correct the noise covariances for ensure the robustness of the state estimation algorithm. Finally, the RASCKF algorithm is verified by simulation under different noise interference conditions. The results show that RASCKF algorithm is superior to standard SCKF algorithm in estimation accuracy and stability, which effectively solves the problem of uncertain noise statistical characteristics in the process of intelligent vehicle target state tracking.

Key words: intelligent vehicle, target state tracking, square-root cubature Kalman filter, robust adaptive

CLC Number:

U461

ZHANG Zhida, ZHENG Ling, LI Yinong, WU Hang, YU Yinghong. Research on Intelligent Vehicle Target State Tracking Based on Robust Adaptive SCKF[J]. Journal of Mechanical Engineering, 2021, 57(20): 181-193.

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