A Novel Comprehensive Scheme for Vehicle State Estimation Using Strong Tracking H-infinity EKF

Accurate acquisition of key vehicle states is essential to the active safety of vehicles. However, existing estimation algorithms cannot deal with the degradation of vehicle state estimation caused by model mutation and unknown noise. In this research, a strong tracking H-infinity extended Kalman filter (STHEKF) is proposed to estimate vehicle states. Strong tracking filter is introduced to update the gain matrix, which greatly enhances the estimation accuracy under time-varying model parameters. The H-infinity filter is adopted to estimate the linear combination of discrete vehicle states and J-infinity function is defined as the standard to evaluate the estimation performance. To demonstrate the effectiveness of STHEKF, simulation tests are carried out under different driving conditions. Results show that STHEKF has a higher estimation accuracy than EKF and STEKF and shows a strong robustness to various road conditions.