Maximum correntropy delay Kalman filter for SINS/USBL integrated navigation

Xu, Bo; Wang, Xiaoyu; Jiao, Zhang; Razzaqi, Asghar A.

doi:10.1016/j.isatra.2021.01.055

Cited by 31 publications

(4 citation statements)

References 33 publications

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“…Allowing the correntropy as an objective function has a strong suppression effect on non-Gaussian heavy-tailed noise [ 25 ]. If the error data

can be obtained, the MCC-based objective function is expressed as

…”

Section: Robust Hierarchical Estimation Schemementioning

confidence: 99%

“…Recently, the maximum correntropy criterion (MCC) in information theory has been introduced into the Kalman filter to deal with problems caused by non-Gaussian noise. The robust Kalman filter based on MCC has been successfully applied in other non-Gaussian scenarios and has shown excellent performance [ 25 , 26 ]. In particular, the maximum correntropy square-root cubature Kalman filter (MCSCKF) not only guarantees high accuracy and numerical stability but also suppresses the interference of non-Gaussian noise [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

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

Feng

et al. 2023

Entropy

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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.

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“…Allowing the correntropy as an objective function has a strong suppression effect on non-Gaussian heavy-tailed noise [ 25 ]. If the error data

can be obtained, the MCC-based objective function is expressed as

…”

Section: Robust Hierarchical Estimation Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Feng

et al. 2023

Entropy

View full text Add to dashboard Cite

show abstract

“…As a matter of fact, the noise is often disturbed and shows a non-Gaussian distribution due to the actual measurement conditions (zero bias, random walk errors, temperatureinduced errors, etc.). For instance, due to the presence of large outliers in the actual measurement process, the measured value of the sensor is often interrupted by heavy-tailed noise [38]. Figure 2 illustrates the amplitude and probability density distribution of Gaussian noise and heavy-tailed noise in the time domain.…”

Section: Discretization Of the State Equation And The Measure-mentioning

confidence: 99%

“…As an information-theoretic quantity, correntropy captures higher-order statistics of the error rather than the common second-order statistics and has been extensively employed in many fields [32][33][34][35][36][37]. In particular, the robust Kalman filter based on the maximum correntropy criterion (MCC) has been successfully implemented in various non-Gaussian environments [38][39][40][41][42]. For this study, we apply the conventional Kalman filter based on MCC to the field of vehicle state estimation.…”

Section: Introductionmentioning

confidence: 99%

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

Qi¹,

Feng²,

Wan³

et al. 2022

Meas. Sci. Technol.

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For vehicle state estimation, conventional Kalman filters work well under Gaussian assumptions. Still, they are likely to degrade dramatically in the practical non-Gaussian situation (especially the noise is heavy-tailed), showing poor accuracy and robustness. This article presents an estimation technique based on the Maximum Correntropy Criterion (MCC) combined with an adaptive extended Kalman filter (AEKF), and an extended Kalman filter (EKF) based on the MCC has also been studied. A lateral-longitudinal coupled vehicle model is developed, while an observer containing the state vectors such as yaw rate, sideslip angle, vehicle velocity and tire cornering stiffness is designed using easily available in-vehicle sensors and low-cost GPS. After analyzing the algorithmic complexity, the proposed algorithm is validated by Sine Steering Input and Double Lane Change driving scenarios. Finally, it is found that MCC combined with AEKF/EKF (MCAEKF/MCEKF) has stronger robustness and better estimation accuracy than AEKF/EKF in dealing with non-Gaussian noise for vehicle state estimation.

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A Novel Outlier-Robust Filter with Gaussian Multi-scale Mixture Model

Huang

Liu

et al. 2023

Lecture Notes in Electrical Engineering

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Maximum correntropy delay Kalman filter for SINS/USBL integrated navigation

Cited by 31 publications

References 33 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

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

A Novel Outlier-Robust Filter with Gaussian Multi-scale Mixture Model

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