摘要
For multi-mode uncertainty of system state evolution and diversity of state constraints, an interactive multihypothesis estimation method for Markov switched systems with jump constraints is proposed. The hypothesis set containing the possible values of the jump Markov parameter is defined. According to the optimal Bayesian filtering, the recursive update of the posterior probability of the state and hypothesis is derived. Based on statistical linear regression, the pseudo measurement method is used to extend the linearized constraint to the real measurement, and the approximate analytical optimal solution of the nonlinear system filtering is given. Finally, an approximate optimal estimation of the sparse grid integral algorithm is presented. In the simulation scenario of crossing road maneuvering target tracking, the filtering accuracy of the proposed algorithm is better than that of the interactive multi-model algorithm based on Taylor expansion, the interactive multi-model algorithm based on statistical linear regression, and the constrained filtering algorithm for nonlinear systems based on Taylor expansion.
投稿的翻译标题 | Nonlinear filtering for Markov switched systems under jump constraints |
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源语言 | 繁体中文 |
页(从-至) | 643-652 |
页数 | 10 |
期刊 | Kongzhi Lilun Yu Yingyong/Control Theory and Applications |
卷 | 39 |
期 | 4 |
DOI | |
出版状态 | 已出版 - 4月 2022 |
关键词
- Interactive multi-model
- Jump constraints
- Linear statistical regression
- Markov switched systems
- Pseudo-measurement
- State estimation