Centralized fusion robust filtering for networked uncertain systems with colored noises, one-step random delay, and packet dropouts
Abstract This paper studies the estimation problem for multisensor networked systems with mixed uncertainties, which include colored noises, same multiplicative noises in system parameter matrices, uncertain noise variances, as well as the one-step random delay (OSRD) and packet dropouts (PDs). This...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-03-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-022-00857-4 |
| _version_ | 1818358400672595968 |
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| author | Shuang Li Wenqiang Liu Guili Tao |
| author_facet | Shuang Li Wenqiang Liu Guili Tao |
| author_sort | Shuang Li |
| collection | DOAJ |
| description | Abstract This paper studies the estimation problem for multisensor networked systems with mixed uncertainties, which include colored noises, same multiplicative noises in system parameter matrices, uncertain noise variances, as well as the one-step random delay (OSRD) and packet dropouts (PDs). This study utilizes the centralized fusion (CF) algorithm to combing all information received by each sensor, which improve the accuracy of the estimation. By using the augmentation method, de-randomization method and fictitious noise techniques, the original uncertain system is transformed into an augment model with only uncertain noise variances. Then, for all uncertainties within the allowable range, the robust CF steady-state Kalman estimators (predictor, filter, and smoother) are presented based on the worst-case CF system, in light of the minimax robust estimation principle. To demonstrate the robustness of the proposed CF estimators, the non-negative definite matrix decomposition method and Lyapunov equation approach are employed. It is proved that the robust accuracy of CF estimator is higher than that of each local estimator. Finally, the simulation example applied to the uninterruptible power system (UPS) with colored noises and multiple uncertainties illustrates the effectiveness of the proposed CF robust estimation algorithm. |
| first_indexed | 2024-12-13T20:28:24Z |
| format | Article |
| id | doaj.art-33d289903ca3472ea599a2183e859fc9 |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-12-13T20:28:24Z |
| publishDate | 2022-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-33d289903ca3472ea599a2183e859fc92022-12-21T23:32:29ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802022-03-012022112310.1186/s13634-022-00857-4Centralized fusion robust filtering for networked uncertain systems with colored noises, one-step random delay, and packet dropoutsShuang Li0Wenqiang Liu1Guili Tao2School of Information and Electronic Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang UniversitySchool of Information and Electronic Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang UniversityCollege of Media Engineering, Communication University of ZhejiangAbstract This paper studies the estimation problem for multisensor networked systems with mixed uncertainties, which include colored noises, same multiplicative noises in system parameter matrices, uncertain noise variances, as well as the one-step random delay (OSRD) and packet dropouts (PDs). This study utilizes the centralized fusion (CF) algorithm to combing all information received by each sensor, which improve the accuracy of the estimation. By using the augmentation method, de-randomization method and fictitious noise techniques, the original uncertain system is transformed into an augment model with only uncertain noise variances. Then, for all uncertainties within the allowable range, the robust CF steady-state Kalman estimators (predictor, filter, and smoother) are presented based on the worst-case CF system, in light of the minimax robust estimation principle. To demonstrate the robustness of the proposed CF estimators, the non-negative definite matrix decomposition method and Lyapunov equation approach are employed. It is proved that the robust accuracy of CF estimator is higher than that of each local estimator. Finally, the simulation example applied to the uninterruptible power system (UPS) with colored noises and multiple uncertainties illustrates the effectiveness of the proposed CF robust estimation algorithm.https://doi.org/10.1186/s13634-022-00857-4Centralized fusionMultisensor networked systemColored noisesMinimax robust estimation principleOne-step random delayPacket dropouts |
| spellingShingle | Shuang Li Wenqiang Liu Guili Tao Centralized fusion robust filtering for networked uncertain systems with colored noises, one-step random delay, and packet dropouts EURASIP Journal on Advances in Signal Processing Centralized fusion Multisensor networked system Colored noises Minimax robust estimation principle One-step random delay Packet dropouts |
| title | Centralized fusion robust filtering for networked uncertain systems with colored noises, one-step random delay, and packet dropouts |
| title_full | Centralized fusion robust filtering for networked uncertain systems with colored noises, one-step random delay, and packet dropouts |
| title_fullStr | Centralized fusion robust filtering for networked uncertain systems with colored noises, one-step random delay, and packet dropouts |
| title_full_unstemmed | Centralized fusion robust filtering for networked uncertain systems with colored noises, one-step random delay, and packet dropouts |
| title_short | Centralized fusion robust filtering for networked uncertain systems with colored noises, one-step random delay, and packet dropouts |
| title_sort | centralized fusion robust filtering for networked uncertain systems with colored noises one step random delay and packet dropouts |
| topic | Centralized fusion Multisensor networked system Colored noises Minimax robust estimation principle One-step random delay Packet dropouts |
| url | https://doi.org/10.1186/s13634-022-00857-4 |
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