Estimation of Vehicle Status and Parameters Based on Nonlinear Kalman Filtering

Huang, Yuhao

doi:10.1109/icras55217.2022.9842063

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Then the structure of the classical Kalman filtering algorithm is used to implement its filtering process 8 . The extended Kalman filter algorithm can be divided into two parts: the prediction process, which is based on the state of the system at the current moment, and the prediction estimate for the next moment obtained from the state equation and the process noise covariance matrix; The calibration process is to combine the measurement results with the prediction estimates to obtain the optimal estimate of the system 9 . The specific flow of the extended Kalman filtering algorithm is shown in Figure 2 10 .…”

Section: Extended Kalman Filter Principlementioning

confidence: 99%

Extended kalman filter based state estimation of formula student autonomous racing

WANG

2023

Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023)

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Obtaining accurate real-time vehicle status information is an important prerequisite for decision-making and control of formula student autonomous racing, but some status information is difficult to obtain in real time through direct measurement. To address these issues, this paper presents a method for state estimation of formula student autonomous racing based on a three-degree-of-freedom model of the vehicle using the extended Kalman filter which is validated and analyzed by joint simulation with CarSim and MATLAB/Simulink. The results show that the scheme can estimate the sideslip angle and yaw angular velocity of the racing in real time and more accurately, and the estimation results provide a parametric basis for the racing control system.

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Section: Extended Kalman Filter Principlementioning

confidence: 99%

Extended kalman filter based state estimation of formula student autonomous racing

WANG

2023

Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023)

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“…It can eliminate the process of calculating complex Jacobi matrices, and its accuracy is higher in dealing with more complex nonlinear problems [18,19]. Huang Yuhao et al [20] compared UKF with EKF, proving that UKF has a higher accuracy. In addition, many scholars have also studied state estimation based on PF [21,22].…”

Section: Introductionmentioning

confidence: 99%

Vehicle State Estimation Based on Sage–Husa Adaptive Unscented Kalman Filtering

Chen,

Yan,

2023

WEVJ

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To combat the impacts of uncertain noise on the estimation of vehicle state parameters and the high cost of sensors, a state-observer design with an adaptive unscented Kalman filter (AUKF) is developed. The design equation of the state observer is derived by establishing the vehicle’s three degrees-of-freedom (DOF) model. On this basis, the Sage–Husa algorithm and unscented Kalman filter (UKF) are combined to form the AUKF algorithm to adaptively update the statistical feature estimation of measurement noise. Finally, a co-simulation using Carsim and Matlab/Simulink confirms the algorithm is effective and reasonable. The simulation results demonstrate that the proposed algorithm, compared with the UKF algorithm, increases estimation accuracy by 19.13%, 32.8%, and 39.46% in yaw rate, side-slip angle, and longitudinal velocity, respectively. This is because the proposed algorithm adaptively adjusts the measurement noise covariance matrix, which can estimate the state parameters of the vehicle more accurately.

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Automated Teller Machine Security and Robbery Prevention Based on Human Behaviour Analysis

2023

2023 Innovations in Power and Advanced Computing Technologies (I-Pact)

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Estimation of Vehicle Status and Parameters Based on Nonlinear Kalman Filtering

Cited by 3 publications

References 8 publications

Extended kalman filter based state estimation of formula student autonomous racing

Extended kalman filter based state estimation of formula student autonomous racing

Vehicle State Estimation Based on Sage–Husa Adaptive Unscented Kalman Filtering

Automated Teller Machine Security and Robbery Prevention Based on Human Behaviour Analysis

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