A novel maximum correntropy adaptive extended Kalman filter for vehicle state estimation under non-Gaussian noise

Qi, Dengliang; Feng, Jianxiang; Wan, Wenkang; Song, Bao

doi:10.1088/1361-6501/aca172

Cited by 15 publications

(9 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The conventional Kalman filter is based on the MMSE criterion and assumes that the noise is Gaussian distributed, which has good performance in a Gaussian environment. However, in actual vehicle operation, the sensor signals are vulnerable to large outliers, and the noises are often heavy-tailed with non-Gaussian characteristics, etc., which degrades the performance of the conventional Kalman filter [ 32 ]. MCC can capture the second- and higher-order moments of the errors and has a strong suppression effect on non-Gaussian noises [ 24 ].…”

Section: Robust Hierarchical Estimation Schemementioning

confidence: 99%

A Robust Hierarchical Estimation Scheme for Vehicle State Based on Maximum Correntropy Square-Root Cubature Kalman Filter

Feng

et al. 2023

Entropy

Self Cite

View full text Add to dashboard Cite

Accurate acquisition of vehicle dynamics state information is essential for vehicle active safety control systems. However, these states cannot be easily measured, and the measurement is expensive. Conventional Kalman filters perform well for vehicle state estimation in Gaussian environments but exhibit low accuracy and robustness under practical non-Gaussian noise. Vehicle model parameter ingestion, inaccurate tire force calculation, and non-Gaussian noise from on-board sensors cause great challenges to the estimation of vehicle driving states. Therefore, this paper presents a robust hierarchical estimation scheme for vehicle driving state based on the maximum correntropy square-root cubature Kalman filter (MCSCKF) using easily measurable on-board sensor information. First, the vehicle mass is dynamically updated based on the recursive least squares (FRLS) method with a forgetting factor. Then, an adaptive sliding mode observer (ASMO) is designed to estimate the longitudinal and lateral tire forces. Ultimately, the vehicle states are estimated based on the MCSCKF under non-Gaussian noise. Two typical operating situations are carried out to verify the validity of the proposed estimation scheme. The results prove that the proposed estimation scheme can estimate the vehicle’s driving state accurately compared to other common methods. And the MCSCKF algorithm has better accuracy and robustness than the traditional Kalman filters for vehicle state estimation in non-Gaussian situations.

show abstract

Section: Robust Hierarchical Estimation Schemementioning

confidence: 99%

A Robust Hierarchical Estimation Scheme for Vehicle State Based on Maximum Correntropy Square-Root Cubature Kalman Filter

Feng

et al. 2023

Entropy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ziegler et al [45] proposed a Kalman filter (KF) estimator to estimate the TM charge, which has high estimation accuracy in a linear system. The KF is a recursive filter that can obtain an optimal estimation of the linear system state by minimizing the mean square error (MSE) of the estimation error [46,47]. It is widely used in automation, positioning, target tracking, communication and signal processing, digital image processing, speech signal processing, earthquake prediction and many other engineering fields [48].…”

Section: Introductionmentioning

confidence: 99%

Test mass charge estimation for the space inertial sensor with extended Kalman filter

Gu,

Cheng,

Hong

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Charge Management System (CMS), aimed at mitigating charge-induced noise on an isolated free-falling test mass (TM), is a crucial component of space inertial sensors in various spaceborne gravitational missions. The estimation of TM charge is one of the tasks of CMS, directly impacting CMS performance. However, current methods for TM charge estimation suffer from slow response and significant estimation errors. This paper presents a new charge estimation method that combines the force modulation principle with an extended Kalman filter (EKF). We analyzed the relationship between the TM surface potential and TM motion, then established a mathematical model. The estimation of TM charge is accomplished through the EKF algorithm. A charge estimation simulation model was developed in Simulink, and experiments were conducted to evaluate the performance of the charge estimation methods under varying charge conditions. The experimental results demonstrate that the EKF method obtains more accurate estimation results and faster convergence rates, compared with Kalman Filter (KF) and Quadrature Demodulation (QD) methods. The KF and QD methods show larger errors when the estimated charge is greater than 106e order, meanwhile the QD method exhibits slower convergence rates. The presented method is expected to be beneficial for the CMS of on-orbit inertial sensors.

show abstract

“…In recent years, the maximum correntropy criterion (MCC) for kernel functions has been successfully applied in the field of robust signal filtering [20]. An adaptive EKF method based on MCC is proposed, which solves the SE problem under heavy tail noise more efficiently [21]. Also based on the improved filtering method of MCC, a SE method that combines MCC with cubature Kalman filtering is proposed where the method also performs well in the presence of abnormal noise in synchronous motors [22].…”

Section: Introductionmentioning

confidence: 99%

A dynamic state estimation method for integrated energy system based on radial basis kernel function

Chen,

Luo,

Foo

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

For state estimation of dynamic electro-thermal gas coupled systems, measurements usually assume that the measurement noise obeys a Gaussian distribution. The extended Kalman filter (EKF) and the unscented Kalman filter (UKF) are some of the widely used estimation methods in state estimation. However, the measurement noise does not always follow Gaussian distribution in practice. When the measurement noise is non-Gaussian, the performance of these methods may not be satisfactory. In this paper, we propose an unscented Kalman filtering method based on minimising radial basis kernel function criterion (MRBFC-UKF), which explores the optimal values of the shape parameters of the kernel function instead of using the widely used Gaussian and exponential kernal functions. Simulations are run dynamically in an integrated energy system which comprises an IEEE 14-bus, a 20-node natural gas network and a 32-node local thermal network. The results show that the proposed method has good robustness and accuracy, and can effectively cope with the presence of unexpected bad data inputs.

show abstract

A novel maximum correntropy adaptive extended Kalman filter for vehicle state estimation under non-Gaussian noise

Cited by 15 publications

References 46 publications

A Robust Hierarchical Estimation Scheme for Vehicle State Based on Maximum Correntropy Square-Root Cubature Kalman Filter

A Robust Hierarchical Estimation Scheme for Vehicle State Based on Maximum Correntropy Square-Root Cubature Kalman Filter

Test mass charge estimation for the space inertial sensor with extended Kalman filter

A dynamic state estimation method for integrated energy system based on radial basis kernel function

Contact Info

Product

Resources

About