Multi‐sensor particle filtering with multi‐step randomly delayed measurements
Abstract This paper develops particle filtering for multi‐sensor systems with randomly delayed measurements, where the general case that random delay can be multi‐step rather than one‐step or two‐step is considered. Moreover, different sensors can have different delay steps and delay probabilities....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | IET Science, Measurement & Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/smt2.12004 |
| _version_ | 1797901533214408704 |
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| author | Yunqi Chen Zhibin Yan |
| author_facet | Yunqi Chen Zhibin Yan |
| author_sort | Yunqi Chen |
| collection | DOAJ |
| description | Abstract This paper develops particle filtering for multi‐sensor systems with randomly delayed measurements, where the general case that random delay can be multi‐step rather than one‐step or two‐step is considered. Moreover, different sensors can have different delay steps and delay probabilities. Random delays are assumed to be mutually independent for different sensors and modelled by a separate sequence of random variables obeying discrete distributions. Since random delay leads to the actual measurements being dependent rather than independent given states, and this dependence becomes more complicated with the increase of random delay step, a new formula of the local likelihood density is proposed and then a new weighting scheme is adopted in particle filtering to deal with these difficulties. The proposed method is applied to two examples to testify its effectiveness and superiority. |
| first_indexed | 2024-04-10T09:03:26Z |
| format | Article |
| id | doaj.art-8d41a669d9394f958b3f6853e359f8d3 |
| institution | Directory Open Access Journal |
| issn | 1751-8822 1751-8830 |
| language | English |
| last_indexed | 2024-04-10T09:03:26Z |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Science, Measurement & Technology |
| spelling | doaj.art-8d41a669d9394f958b3f6853e359f8d32023-02-21T09:05:56ZengWileyIET Science, Measurement & Technology1751-88221751-88302021-01-01151354310.1049/smt2.12004Multi‐sensor particle filtering with multi‐step randomly delayed measurementsYunqi Chen0Zhibin Yan1Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin People's Republic of ChinaSchool of Science Harbin Institute of Technology Shenzhen People's Republic of ChinaAbstract This paper develops particle filtering for multi‐sensor systems with randomly delayed measurements, where the general case that random delay can be multi‐step rather than one‐step or two‐step is considered. Moreover, different sensors can have different delay steps and delay probabilities. Random delays are assumed to be mutually independent for different sensors and modelled by a separate sequence of random variables obeying discrete distributions. Since random delay leads to the actual measurements being dependent rather than independent given states, and this dependence becomes more complicated with the increase of random delay step, a new formula of the local likelihood density is proposed and then a new weighting scheme is adopted in particle filtering to deal with these difficulties. The proposed method is applied to two examples to testify its effectiveness and superiority.https://doi.org/10.1049/smt2.12004Filtering methods in signal processingSignal processing theorySensor fusionOther topics in statisticsOther topics in statistics |
| spellingShingle | Yunqi Chen Zhibin Yan Multi‐sensor particle filtering with multi‐step randomly delayed measurements IET Science, Measurement & Technology Filtering methods in signal processing Signal processing theory Sensor fusion Other topics in statistics Other topics in statistics |
| title | Multi‐sensor particle filtering with multi‐step randomly delayed measurements |
| title_full | Multi‐sensor particle filtering with multi‐step randomly delayed measurements |
| title_fullStr | Multi‐sensor particle filtering with multi‐step randomly delayed measurements |
| title_full_unstemmed | Multi‐sensor particle filtering with multi‐step randomly delayed measurements |
| title_short | Multi‐sensor particle filtering with multi‐step randomly delayed measurements |
| title_sort | multi sensor particle filtering with multi step randomly delayed measurements |
| topic | Filtering methods in signal processing Signal processing theory Sensor fusion Other topics in statistics Other topics in statistics |
| url | https://doi.org/10.1049/smt2.12004 |
| work_keys_str_mv | AT yunqichen multisensorparticlefilteringwithmultisteprandomlydelayedmeasurements AT zhibinyan multisensorparticlefilteringwithmultisteprandomlydelayedmeasurements |