An Enhanced Adaptive Unscented Kalman Filter for Vehicle State Estimation

Accurate vehicle state information is crucial for safe driving and dynamic control of vehicles. Vehicle state estimation under unknown noise conditions is an important research topic. A state estimation method based on enhanced adaptive unscented Kalman filter (EAUKF) is proposed to solve vehicle es...

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Vydáno v:IEEE transactions on instrumentation and measurement Ročník 71; s. 1 - 12
Hlavní autoři: Zhang, Yingjie, Li, Ming, Zhang, Ying, Hu, Zuolei, Sun, Qingshuai, Lu, Biliang
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accuracy
Adaptive algorithms
Adaptive modulation factor
Algorithms
Attenuation
Dynamic control
enhanced adaptive unscented Kalman filter (EAUKF)
Force
Kalman filters
longitudinal dynamics modeling
Mathematical models
Roads
safe driving and dynamic control
State estimation
State variable
Tires
Vehicle dynamics
vehicle state estimation
Wheels
ISSN:0018-9456, 1557-9662
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Shrnutí:Accurate vehicle state information is crucial for safe driving and dynamic control of vehicles. Vehicle state estimation under unknown noise conditions is an important research topic. A state estimation method based on enhanced adaptive unscented Kalman filter (EAUKF) is proposed to solve vehicle estimation under unknown noise conditions. The general exponential attenuation adaptive Kalman filter algorithm does not attenuate the historical data enough when the noise statistics change rapidly, thus leading to the state variable's inaccurate estimation. To improve the estimation accuracy of vehicle state variables, the exponential attenuation factor <inline-formula> <tex-math notation="LaTeX">B </tex-math></inline-formula> was further designed according to the variation of noise variance, and the influence of the latest data on state estimation was more considered. Based on the longitudinal dynamics modeling, the EAUKF method is applied to vehicle state estimation. Compared with the standard exponential weighted adaptive Kalman filtering algorithm and the average weighted adaptive Kalman filtering algorithm, the state variable estimation accuracy of the vehicle in this article is improved.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2022.3180407