Kalman Filter Slope Measurement Method Based on Improved Genetic Algorithm-Back Propagation

Wang, Haoyu; Guo, Donghua; Tan, Gangfeng; Wang, Zhenyu; Li, Ming; Jiang, Yifeng; Meng, Yu; Chen, Kailang

doi:10.4271/2020-01-0897

Cited by 2 publications

(1 citation statement)

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“…The commonly used vehicle state parameter estimation methods include Kalman filter (KF) and its improved algorithms, [7][8][9][10][11][12][13][14][15][16][17] neural network estimation algorithms, [18][19][20] and other related estimation algorithms. [21][22][23][24][25][26][27] However, single estimation algorithms have their own limitations, such as the uncertainty of mathematical model parameters, noise parameters, and the coverage of training samples, which will affect the estimation results and may lead to the sudden divergence of estimation accuracy. 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.…”

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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