Iterative Truncated Unscented Particle Filter
The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimation performance compared to unconstrained state es...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/11/4/214 |
| _version_ | 1797570583264755712 |
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| author | Yanbo Wang Fasheng Wang Jianjun He Fuming Sun |
| author_facet | Yanbo Wang Fasheng Wang Jianjun He Fuming Sun |
| author_sort | Yanbo Wang |
| collection | DOAJ |
| description | The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimation performance compared to unconstrained state estimation. This paper introduces an iterative truncated unscented particle filter, which provides a state estimation method with inequality constraints. In this method, the proposal distribution is generated by an iterative unscented Kalman filter that is supplemented with a designed truncation method to satisfy the constraints. The detailed iterative unscented Kalman filter and truncation method is provided and incorporated into the particle filter framework. Experimental results show that the proposed algorithm is superior to other similar algorithms. |
| first_indexed | 2024-03-10T20:26:34Z |
| format | Article |
| id | doaj.art-e1de4e1f5c574af18ff5b2078c335450 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T20:26:34Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-e1de4e1f5c574af18ff5b2078c3354502023-11-19T21:45:57ZengMDPI AGInformation2078-24892020-04-0111421410.3390/info11040214Iterative Truncated Unscented Particle FilterYanbo Wang0Fasheng Wang1Jianjun He2Fuming Sun3School of Information and Communication Engineering, Dalian Minzu University, Dalian 116620, ChinaSchool of Information and Communication Engineering, Dalian Minzu University, Dalian 116620, ChinaSchool of Information and Communication Engineering, Dalian Minzu University, Dalian 116620, ChinaSchool of Information and Communication Engineering, Dalian Minzu University, Dalian 116620, ChinaThe particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimation performance compared to unconstrained state estimation. This paper introduces an iterative truncated unscented particle filter, which provides a state estimation method with inequality constraints. In this method, the proposal distribution is generated by an iterative unscented Kalman filter that is supplemented with a designed truncation method to satisfy the constraints. The detailed iterative unscented Kalman filter and truncation method is provided and incorporated into the particle filter framework. Experimental results show that the proposed algorithm is superior to other similar algorithms.https://www.mdpi.com/2078-2489/11/4/214state estimationparticle filteriterative unscented Kalman filteriterative truncated particle filter |
| spellingShingle | Yanbo Wang Fasheng Wang Jianjun He Fuming Sun Iterative Truncated Unscented Particle Filter Information state estimation particle filter iterative unscented Kalman filter iterative truncated particle filter |
| title | Iterative Truncated Unscented Particle Filter |
| title_full | Iterative Truncated Unscented Particle Filter |
| title_fullStr | Iterative Truncated Unscented Particle Filter |
| title_full_unstemmed | Iterative Truncated Unscented Particle Filter |
| title_short | Iterative Truncated Unscented Particle Filter |
| title_sort | iterative truncated unscented particle filter |
| topic | state estimation particle filter iterative unscented Kalman filter iterative truncated particle filter |
| url | https://www.mdpi.com/2078-2489/11/4/214 |
| work_keys_str_mv | AT yanbowang iterativetruncatedunscentedparticlefilter AT fashengwang iterativetruncatedunscentedparticlefilter AT jianjunhe iterativetruncatedunscentedparticlefilter AT fumingsun iterativetruncatedunscentedparticlefilter |