A Robust Unscented M-Estimation-Based Filter for Vehicle State Estimation With Unknown Input

Longitudinal velocity, lateral velocity, and front wheel steering angle are crucial states for vehicle active safety features. However, direct measurement of these variables requires expensive measurement instruments and can be easily affected by vehicle nonlinear dynamics, outliers and noise pollut...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2022-06, Vol.71 (6), p.6119-6130
Main Authors: Xue, Zhongjin, Cheng, Shuo, Li, Liang, Zhong, Zhihua, Mu, Hongyuan
Format: Article
Language:English
Subjects:
Algorithms
Dynamical systems
Extended Kalman filter
Measuring instruments
Noise pollution
Nonlinear dynamics
Nonlinear systems
Outliers (statistics)
Redundancy
robust unscented filter
Robustness
Safety
Sensors
State estimation
Statistical analysis
Steering
Tires
Torque
torque estimation
unknown input estimation
Vehicle dynamics
Vehicle state estimation
Vehicles
Wheels
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Summary:Longitudinal velocity, lateral velocity, and front wheel steering angle are crucial states for vehicle active safety features. However, direct measurement of these variables requires expensive measurement instruments and can be easily affected by vehicle nonlinear dynamics, outliers and noise pollution. Moreover, unknown inputs bring great challenges to their accurate estimation. Therefore, this paper develops a novel robust unscented M-estimation-based filter (RUMF) for vehicle state estimation with unknown driver steering torque. The nonlinear system model is constructed based on the vehicle dynamics model. Unscented transformation (UT) and statistical linearization are implemented to transform the nonlinear process and measurement function into a linear-like regression form with data redundancy to achieve outlier suppression. Then, an M-estimation-based iterated algorithm is designed to address process uncertainty and innovation and observation outliers for robust vehicle state estimation. The iteratively reweighted least-squares (IRLS) method based on the M-estimation methodology is utilized for unknown input estimation. Simulations and experimental results compared with extended Kalman filter (EKF) and particle filter (PF) have verified the effectiveness and robustness of the proposed algorithm.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2022.3163207