Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter

Abstract

The tracking accuracy of the traditional Strong Tracking Unscented Kalman Filter algorithm (ST-UKF) decreases when the motion state of the traffic target changes significantly. A multidimensional adaptive factor-based strong tracking UKF (MAST-UKF) algorithm is proposed. The method introduces multidimensional attenuation factors in the prediction and updating process of filtering, and realizes the strong tracking filtering of vehicle targets by adjusting the uncertainty of state noise covariance and observation noise covariance and dynamically updating the multidimensional attenuation factors by adaptively adjusting the threshold based on the observation residuals and the state estimation error. Target tracking simulations are performed under system model uncertainty, and the tracking errors of MAST-UKF are reduced by 32.67%, 28.54%, and 23.17% compared to UKF, ST-UKF, and AST-UKF, respectively. The real vehicle experiments show that MAST-UKF reduces the distance error by 18.29% and speed error by 15.25% compared to AST-UKF. The results demonstrate that the MAST-UKF algorithm is able to adaptively adjust the noise covariance and effectively cope with the inaccuracy of the state noise and observation noise, thus realizing the accurate tracking of the target under complex conditions.