Parallel Kalman filter group integrated particle filter method for the train nonlinear operational status high-precision estimation under non-Gaussian environment

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Parallel Kalman filter group integrated particle filter method for the train nonlinear operational status high-precision estimation under non-Gaussian environment

Wen, Tao / Liu, Jinzhuo / Cao, Yuan / Roberts, Clive

Abstract For the problem of multi-mode state estimation in actual train operation, this paper proposes a nonlinear non-gaussian high-precision parallel Kalman filter group (NN-HEKFG) integrated Particle Filter. A multi-model Gaussian decomposition of the probability density function for state equations and measurement equations is performed, and each local state model is represented by a multi-dimensional high-order polynomial to establish the expanded dimensional state model. Then, by updating the mean and variance of the local state expanded dimensional model and in turn solving the particle filtering posterior probability density distribution function, the global estimation results are obtained. In reducing the number of Gaussian terms, a new parameter reduction criterion is established, which can effectively carry out the re-identification of parameters such as weights and means, so as to avoid the problem of parameter explosion. The superiority of NN-HEKFG over particle filters and Gaussian sum filters and its effectiveness for train running state estimation are verified by simulating the multi-model running state of trains.

Highlights • More accurate estimation for the train nonlinear operational status under non-Gaussian environment. • A better way to solve the mean and variance of the posterior distribution function in Particle Filter estimation. • Reduced truncation error in Taylor expansion is achieved.

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Title :

Parallel Kalman filter group integrated particle filter method for the train nonlinear operational status high-precision estimation under non-Gaussian environment

Contributors:

Wen, Tao (author) / Liu, Jinzhuo (author) / Cao, Yuan (author) / Roberts, Clive (author)

Published in:

Accident Analysis and Prevention ; 190

Publication date :

2023-06-06

ISSN :

0001-4575

DOI :

https://doi.org/10.1016/j.aap.2023.107158

Type of media :

Article (Journal)