Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter

Wenkang, Wan; Jingan, Feng; Bao, Song; Xinxin, Li

doi:10.3390/app112210772

Cited by 16 publications

(11 citation statements)

References 26 publications

(26 reference statements)

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“…Real-time acquisition of these parameters requires costly high-precision sensors, which are highly susceptible to noise interference, leading to measurement errors and uncertainties. Unreliable data can lead to discrete errors in vehicle controllers, resulting in severe consequences [7]. These critical parameter estimates are often based on signals from low-cost sensors [8].…”

Section: Introductionmentioning

confidence: 99%

State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF

Chen,

Zhang,

et al. 2024

Applied Sciences

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The prerequisite for intelligent vehicles to achieve autonomous driving and active safety functions is acquiring accurate vehicle state parameters. Traditional Kalman filters often underperform in non-Gaussian noise environments due to their reliance on Gaussian assumptions. This paper presents the IMM-MCCKF filter, which integrates the interacting multiple model theory (IMM) and the maximum correntropy cubature Kalman filter method (MCCKF), for estimating the state parameters of intelligent vehicles. The IMM-MCCKF successfully suppresses non-Gaussian noise by optimizing a nonlinear cost function using the maximum correntropy criteria, allowing it to capture and analyze signal data outliers accurately. The filter designs various state and measurement noise submodels to adapt to the environment dynamically, thus reducing the impact of unknown noise statistical properties. Accurately measuring the velocity of a vehicle and the angle at which its center of mass drifts sideways is of utmost importance for its ability to maneuver, maintain stability, and ensure safety. These parameters are critical for implementing advanced control systems in intelligent vehicles. The study begins by constructing a nonlinear Dugoff tire model and a three-degrees-of-freedom (3DOF) vehicle model. Subsequently, utilizing low-cost vehicle sensor signals, joint simulations are conducted on the CarSim-Simulink platform to estimate vehicle state parameters. The results demonstrate that in terms of estimation accuracy and robustness in non-Gaussian noise scenarios, the proposed IMM-MCCKF filter consistently outperforms the MCCKF and CKF algorithms.

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Section: Introductionmentioning

confidence: 99%

State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF

Chen,

Zhang,

et al. 2024

Applied Sciences

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“…Some physical or empirical tire models are used to calculate tire forces in vehicle dynamics, e.g., the Dugoff tire model, the magic formula tire model, the brush tire model, etc. [ 8 , 9 , 10 ]. However, the calculation of tire forces requires knowledge of parameters related to tire characteristics, such as tire longitudinal and lateral stiffness, and the acquisition of these parameters often requires extensive prior testing and calibration.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to EKF and UKF, the square-root cubature Kalman filter (SCKF) is widely adopted due to its higher estimation accuracy and better numerical stability. Wan et al combined the interactive multi-model theory with the SCKF to design a vehicle state observer containing multiple sub-models, which reduces the complexity of the algorithm while ensuring accuracy and real-time performance [ 10 ]. Although the above study is reliable, the negative effect of the noise distribution for nonlinear systems on the experimental results is often ignored.…”

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

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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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“…Therefore, for the problems about the single estimation algorithm, researchers integrate different estimation theories to improve the performance of the whole estimation system through the redundancy and fusion of the algorithm. In the study of fusion estimation algorithms, W Wenkang et al 28 use the interacting multiple model method to realize the fusion of two square root cubature KF with different mathematical models. G Liu and LQ Jin 29 use the interacting multiple model method to combine the cubature KF with different mathematical models to estimate the side slip angle and wheel lateral force.…”

Section: Introductionmentioning

confidence: 99%

Vehicle state estimation based on extended Kalman filter and radial basis function neural networks

Zha

Liu

et al. 2022

International Journal of Distributed Sensor Networks

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To improve the reliability of vehicle state parameter estimation, a vehicle state fusion estimation method based on dichotomy is proposed. An extended Kalman filter algorithm is designed based on the vehicle 3 degrees of freedom dynamic model. Meanwhile, considering the influence of dynamic model and sensor noise and its coefficient selection on the estimation results, a radial basis function neural network estimation algorithm is designed. To further improve the reliability of the estimation algorithm, a method of estimation algorithm fusion is proposed based on the idea of mutual compensation between model- and data-driven estimation algorithms. The weights of the estimation results of different algorithms are assigned through the dichotomy. The redundancy and fusion of estimation algorithms can improve estimation performance. The effectiveness of the fusion method is verified by the co-simulation of MATLAB/Simulink and CarSim, and the real vehicle test. The results show that the change trend of the estimation result is consistent with the actual state parameters change trend, and the estimation accuracy after algorithm fusion is significantly improved compared to a single extended Kalman filter or radial basis function.

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Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter

Cited by 16 publications

References 26 publications

State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF

State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF

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

Vehicle state estimation based on extended Kalman filter and radial basis function neural networks

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