Distributed Ellipsoidal Intersection Fusion Estimation for Multi-Sensor Complex Systems
This paper investigates the problem of distributed ellipsoidal intersection (DEI) fusion estimation for linear time-varying multi-sensor complex systems with unknown input disturbances and measurement data transmission delays. For the problem with external unknown input disturbance signals, a non-in...
| Główni autorzy: | , , , |
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| Format: | Artykuł |
| Język: | English |
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MDPI AG
2022-06-01
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| Seria: | Sensors |
| Hasła przedmiotowe: | |
| Dostęp online: | https://www.mdpi.com/1424-8220/22/11/4306 |
| _version_ | 1827663821692469248 |
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| author | Peng Zhang Shuyu Zhou Peng Liu Mengwei Li |
| author_facet | Peng Zhang Shuyu Zhou Peng Liu Mengwei Li |
| author_sort | Peng Zhang |
| collection | DOAJ |
| description | This paper investigates the problem of distributed ellipsoidal intersection (DEI) fusion estimation for linear time-varying multi-sensor complex systems with unknown input disturbances and measurement data transmission delays. For the problem with external unknown input disturbance signals, a non-informative prior distribution is used to model the problem. A set of independent random variables obeying Bernoulli distribution is also used to describe the situation of measurement data transmission delay caused by network channel congestion, and appropriate buffer areas are added at the link nodes to retrieve the delayed transmission data values. For multi-sensor systems with complex situations, a minimum mean square error (MMSE) local estimator is designed in a Bayesian framework based on the maximum a posteriori (MAP) estimation criterion. In order to deal with the unknown correlations among the local estimators and to select the fusion estimator with lower computational complexity, the fusion estimator is designed using ellipsoidal intersection (EI) fusion technique, and the consistency of the estimator is demonstrated. In this paper, the difference between DEI fusion and distributed covariance intersection (DCI) fusion and centralized fusion estimation is analyzed by a numerical example, and the superiority of the DEI fusion method is demonstrated. |
| first_indexed | 2024-03-10T00:50:48Z |
| format | Article |
| id | doaj.art-43644e6b9b514bbe9d49e13eaf5dd6a6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:50:48Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-43644e6b9b514bbe9d49e13eaf5dd6a62023-11-23T14:51:47ZengMDPI AGSensors1424-82202022-06-012211430610.3390/s22114306Distributed Ellipsoidal Intersection Fusion Estimation for Multi-Sensor Complex SystemsPeng Zhang0Shuyu Zhou1Peng Liu2Mengwei Li3School of Instrumentation and Electronic, North University of China, Taiyuan 030051, ChinaSchool of Instrumentation and Electronic, North University of China, Taiyuan 030051, ChinaAcademy for Advanced Interdisciplinary Research, North University of China, Taiyuan 030051, ChinaSchool of Instrumentation and Electronic, North University of China, Taiyuan 030051, ChinaThis paper investigates the problem of distributed ellipsoidal intersection (DEI) fusion estimation for linear time-varying multi-sensor complex systems with unknown input disturbances and measurement data transmission delays. For the problem with external unknown input disturbance signals, a non-informative prior distribution is used to model the problem. A set of independent random variables obeying Bernoulli distribution is also used to describe the situation of measurement data transmission delay caused by network channel congestion, and appropriate buffer areas are added at the link nodes to retrieve the delayed transmission data values. For multi-sensor systems with complex situations, a minimum mean square error (MMSE) local estimator is designed in a Bayesian framework based on the maximum a posteriori (MAP) estimation criterion. In order to deal with the unknown correlations among the local estimators and to select the fusion estimator with lower computational complexity, the fusion estimator is designed using ellipsoidal intersection (EI) fusion technique, and the consistency of the estimator is demonstrated. In this paper, the difference between DEI fusion and distributed covariance intersection (DCI) fusion and centralized fusion estimation is analyzed by a numerical example, and the superiority of the DEI fusion method is demonstrated.https://www.mdpi.com/1424-8220/22/11/4306data fusionunknown input interferencemeasure propagation delayunknown correlation |
| spellingShingle | Peng Zhang Shuyu Zhou Peng Liu Mengwei Li Distributed Ellipsoidal Intersection Fusion Estimation for Multi-Sensor Complex Systems Sensors data fusion unknown input interference measure propagation delay unknown correlation |
| title | Distributed Ellipsoidal Intersection Fusion Estimation for Multi-Sensor Complex Systems |
| title_full | Distributed Ellipsoidal Intersection Fusion Estimation for Multi-Sensor Complex Systems |
| title_fullStr | Distributed Ellipsoidal Intersection Fusion Estimation for Multi-Sensor Complex Systems |
| title_full_unstemmed | Distributed Ellipsoidal Intersection Fusion Estimation for Multi-Sensor Complex Systems |
| title_short | Distributed Ellipsoidal Intersection Fusion Estimation for Multi-Sensor Complex Systems |
| title_sort | distributed ellipsoidal intersection fusion estimation for multi sensor complex systems |
| topic | data fusion unknown input interference measure propagation delay unknown correlation |
| url | https://www.mdpi.com/1424-8220/22/11/4306 |
| work_keys_str_mv | AT pengzhang distributedellipsoidalintersectionfusionestimationformultisensorcomplexsystems AT shuyuzhou distributedellipsoidalintersectionfusionestimationformultisensorcomplexsystems AT pengliu distributedellipsoidalintersectionfusionestimationformultisensorcomplexsystems AT mengweili distributedellipsoidalintersectionfusionestimationformultisensorcomplexsystems |