Multi-sensor fusion estimation subject to random sensor failures under binary encoding scheme: A federated-filtering-based method
This paper explores the issue of fusion estimation in multi-sensor systems experiencing random sensor failures via a binary coding scheme (BCS). The occurrence of sensor failures is modeled using random variables with predetermined probability distributions. To avert the potential signal distortions...
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| Vydané v: | Journal of physics. Conference series Ročník 2898; číslo 1; s. 12029 - 12034 |
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| Hlavní autori: | , , |
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| Jazyk: | English |
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Bristol
IOP Publishing
01.11.2024
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| ISSN: | 1742-6588, 1742-6596 |
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| Abstract | This paper explores the issue of fusion estimation in multi-sensor systems experiencing random sensor failures via a binary coding scheme (BCS). The occurrence of sensor failures is modeled using random variables with predetermined probability distributions. To avert the potential signal distortions during network-based communication, the BCS is utilized to transform the measurement signals into bit strings. A novel federated-filtering-based fusion estimation approach is developed to obtain the desired state estimates. The optimal estimator parameters are achieved by solving a pair of recursive difference equations, taking into account the impacts of bit errors and probabilistic quantization. Additionally, the ultimate boundedness of the estimation error covariance for the fusion estimates is guaranteed by a sufficient condition that we establish. Finally, the utility of the introduced fusion estimation method is illustrated through a simulation example. |
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| AbstractList | This paper explores the issue of fusion estimation in multi-sensor systems experiencing random sensor failures via a binary coding scheme (BCS). The occurrence of sensor failures is modeled using random variables with predetermined probability distributions. To avert the potential signal distortions during network-based communication, the BCS is utilized to transform the measurement signals into bit strings. A novel federated-filtering-based fusion estimation approach is developed to obtain the desired state estimates. The optimal estimator parameters are achieved by solving a pair of recursive difference equations, taking into account the impacts of bit errors and probabilistic quantization. Additionally, the ultimate boundedness of the estimation error covariance for the fusion estimates is guaranteed by a sufficient condition that we establish. Finally, the utility of the introduced fusion estimation method is illustrated through a simulation example. |
| Author | Li, Na Zou, Lei Wang, Chenxi |
| Author_xml | – sequence: 1 givenname: Na surname: Li fullname: Li, Na organization: Engineering Research Center of Digitalized Textile and Fashion Technology , Ministry of Education, Shanghai, 201620, China – sequence: 2 givenname: Lei surname: Zou fullname: Zou, Lei organization: Engineering Research Center of Digitalized Textile and Fashion Technology , Ministry of Education, Shanghai, 201620, China – sequence: 3 givenname: Chenxi surname: Wang fullname: Wang, Chenxi organization: Engineering Research Center of Digitalized Textile and Fashion Technology , Ministry of Education, Shanghai, 201620, China |
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| DOI | 10.1088/1742-6596/2898/1/012029 |
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| References | Zou (JPCS_2898_1_012029bib13) 2023; 54 Li (JPCS_2898_1_012029bib2) 2023; 54 Liu (JPCS_2898_1_012029bib10) 2021; 66 Bernstein (JPCS_2898_1_012029bib12) 1966; 12 Wang (JPCS_2898_1_012029bib4) 2023; 2 Gao (JPCS_2898_1_012029bib7) 2023; 11 Leung (JPCS_2898_1_012029bib11) 2019; 63 Jin (JPCS_2898_1_012029bib3) 2023; 54 Li (JPCS_2898_1_012029bib14) 2013; 61 Geng (JPCS_2898_1_012029bib5) 2020; 7 Li (JPCS_2898_1_012029bib8) 2019; 31 Thummalapeta (JPCS_2898_1_012029bib1) 2023; 54 Xing (JPCS_2898_1_012029bib6) 2016; 63 Hou (JPCS_2898_1_012029bib9) 2022; 9 |
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| SubjectTerms | Binary codes Difference equations Estimates Failure Filtration Multisensor fusion Parameter estimation Random variables Signal distortion Statistical analysis |
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| Title | Multi-sensor fusion estimation subject to random sensor failures under binary encoding scheme: A federated-filtering-based method |
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