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基于马尔科夫算法的车辆行驶工况构建

于 2021-03-02 发布

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代码说明：

说明：用马尔科夫算法对行驶工况的车速进行预测，效果很好。(I use Markov algorithm to predict vehicle speed in driving conditions.)

文件列表：

基于马尔科夫算法的车辆行驶工况构建\code.txt, 6626 , 2019-09-23
基于马尔科夫算法的车辆行驶工况构建\马尔可夫过程图.vsd, 62464 , 2019-09-22
基于马尔科夫算法的车辆行驶工况构建, 0 , 2020-08-01

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This file compares three different versions of the Kalman filter. The Kalman filter is used for recursive parameter estimation. The Kalman filter can handle noisy measurements. The first implemented filter (fcn_KF) is the Kalman filter with standard update of the covariance matrix P. The covariance matrix reflects the uncertainties of the predictions. To improve the numerical stability Potter developed a square root update (fcn_KF_SRP) of the covariance matrix P. Another version is the square root covariance update via triangularization (fcn_KF_SRT). This file generates a model. Then the three Kalman filters perform an estimation of the model parameter. At the end the results are compared. Sources: Simon, D. (2006): Optimal state estimation Kaminski, P. (1971): Discrete Square Root Filtering: A Survey of Current Techniques Golub, G. (1996): Matrix Computations

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基于马尔科夫算法的车辆行驶工况构建

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