Gaussian-Beta Filters With Unknown Probability of Measurement Loss
Data loss is ubiquitous in practical engineering applications due to communication delay or congestion. Data loss rate is a key metric to evaluate the reliability of state estimation. To jointly estimate system state and data loss rate, we propose a class of Gaussian-Beta filters for linear and mode...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9931673/ |
| _version_ | 1828095348906655744 |
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| author | Guanghua Zhang Feng Lian Linghao Zeng Na Fu Shasha Dai Xinqiang Liu |
| author_facet | Guanghua Zhang Feng Lian Linghao Zeng Na Fu Shasha Dai Xinqiang Liu |
| author_sort | Guanghua Zhang |
| collection | DOAJ |
| description | Data loss is ubiquitous in practical engineering applications due to communication delay or congestion. Data loss rate is a key metric to evaluate the reliability of state estimation. To jointly estimate system state and data loss rate, we propose a class of Gaussian-Beta filters for linear and moderate nonlinear Gaussian state-space models with unknown probability of measurement loss. In the filters, the arrival of the measurement at each time is formulated as a binary random variable, which is determined by the classical threshold technology. In addition, the hidden state and the unknown probability of measurement loss are modeled as a product of Gaussian and Beta distributions, and the form remains unchanged through recursive operations. Simulation results verify the effectiveness of the proposed Gaussian-Beta filters compared with the existing filtering algorithms. |
| first_indexed | 2024-04-11T07:12:01Z |
| format | Article |
| id | doaj.art-3a8c574ad3134a60a49a81395f5bc018 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T07:12:01Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3a8c574ad3134a60a49a81395f5bc0182022-12-22T04:38:10ZengIEEEIEEE Access2169-35362022-01-011011512011513010.1109/ACCESS.2022.32177919931673Gaussian-Beta Filters With Unknown Probability of Measurement LossGuanghua Zhang0https://orcid.org/0000-0002-5605-517XFeng Lian1https://orcid.org/0000-0002-5463-715XLinghao Zeng2https://orcid.org/0000-0002-2653-716XNa Fu3Shasha Dai4Xinqiang Liu5https://orcid.org/0000-0003-1980-0721Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Faculty of Electronic and Information Engineering, School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an, ChinaMinistry of Education Key Laboratory for Intelligent Networks and Network Security, Faculty of Electronic and Information Engineering, School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an, ChinaSchool of Economics and Management, Chang’an University, Xi’an, ChinaState Key Laboratory of Astronautic Dynamics, Xi’an Satellite Control Center, Xi’an, ChinaXi’an Satellite Control Center, Xi’an, ChinaBeijing Institute of Electronic System Engineering, Beijing, ChinaData loss is ubiquitous in practical engineering applications due to communication delay or congestion. Data loss rate is a key metric to evaluate the reliability of state estimation. To jointly estimate system state and data loss rate, we propose a class of Gaussian-Beta filters for linear and moderate nonlinear Gaussian state-space models with unknown probability of measurement loss. In the filters, the arrival of the measurement at each time is formulated as a binary random variable, which is determined by the classical threshold technology. In addition, the hidden state and the unknown probability of measurement loss are modeled as a product of Gaussian and Beta distributions, and the form remains unchanged through recursive operations. Simulation results verify the effectiveness of the proposed Gaussian-Beta filters compared with the existing filtering algorithms.https://ieeexplore.ieee.org/document/9931673/State-space modelmeasurement lossthreshold technologyGaussian-Beta filter |
| spellingShingle | Guanghua Zhang Feng Lian Linghao Zeng Na Fu Shasha Dai Xinqiang Liu Gaussian-Beta Filters With Unknown Probability of Measurement Loss IEEE Access State-space model measurement loss threshold technology Gaussian-Beta filter |
| title | Gaussian-Beta Filters With Unknown Probability of Measurement Loss |
| title_full | Gaussian-Beta Filters With Unknown Probability of Measurement Loss |
| title_fullStr | Gaussian-Beta Filters With Unknown Probability of Measurement Loss |
| title_full_unstemmed | Gaussian-Beta Filters With Unknown Probability of Measurement Loss |
| title_short | Gaussian-Beta Filters With Unknown Probability of Measurement Loss |
| title_sort | gaussian beta filters with unknown probability of measurement loss |
| topic | State-space model measurement loss threshold technology Gaussian-Beta filter |
| url | https://ieeexplore.ieee.org/document/9931673/ |
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