Event-Triggered State Filter Estimation for Nonlinear Systems with Packet Dropout and Correlated Noise
This paper begins by exploring the challenge of event-triggered state estimations in nonlinear systems, grappling with packet dropout and correlated noise. A communication mechanism is introduced that determines whether to transmit measurement values based on whether event-triggered conditions are v...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/3/769 |
| _version_ | 1797318258823528448 |
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| author | Guorui Cheng Jingang Liu Shenmin Song |
| author_facet | Guorui Cheng Jingang Liu Shenmin Song |
| author_sort | Guorui Cheng |
| collection | DOAJ |
| description | This paper begins by exploring the challenge of event-triggered state estimations in nonlinear systems, grappling with packet dropout and correlated noise. A communication mechanism is introduced that determines whether to transmit measurement values based on whether event-triggered conditions are violated, thereby minimizing redundant communication data. In designing the filter, noise decorrelation is initially conducted, followed by the integration of the event-triggered mechanism and the unreliable network transmission system for state estimator development. Subsequently, by combining the three-degree spherical–radial cubature rule, the numerical implementation steps of the proposed state estimation framework are outlined. The performance estimation analysis highlights that by adjusting the event-triggered threshold appropriately, the estimation performance and transmission rate can be effectively balanced. It is established that when there is a lower bound on the packet dropout rate, the covariance matrix of the state estimation error remains bounded, and the stochastic stability of the state estimation error is also confirmed. Ultimately, the algorithm and conclusions that are proposed in this paper are validated through a simulation example of a target tracking system. |
| first_indexed | 2024-03-08T03:49:50Z |
| format | Article |
| id | doaj.art-6ea3437d4a984cad9aafa668cf06518b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T03:49:50Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6ea3437d4a984cad9aafa668cf06518b2024-02-09T15:21:47ZengMDPI AGSensors1424-82202024-01-0124376910.3390/s24030769Event-Triggered State Filter Estimation for Nonlinear Systems with Packet Dropout and Correlated NoiseGuorui Cheng0Jingang Liu1Shenmin Song2Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, ChinaCenter for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, ChinaCenter for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, ChinaThis paper begins by exploring the challenge of event-triggered state estimations in nonlinear systems, grappling with packet dropout and correlated noise. A communication mechanism is introduced that determines whether to transmit measurement values based on whether event-triggered conditions are violated, thereby minimizing redundant communication data. In designing the filter, noise decorrelation is initially conducted, followed by the integration of the event-triggered mechanism and the unreliable network transmission system for state estimator development. Subsequently, by combining the three-degree spherical–radial cubature rule, the numerical implementation steps of the proposed state estimation framework are outlined. The performance estimation analysis highlights that by adjusting the event-triggered threshold appropriately, the estimation performance and transmission rate can be effectively balanced. It is established that when there is a lower bound on the packet dropout rate, the covariance matrix of the state estimation error remains bounded, and the stochastic stability of the state estimation error is also confirmed. Ultimately, the algorithm and conclusions that are proposed in this paper are validated through a simulation example of a target tracking system.https://www.mdpi.com/1424-8220/24/3/769correlated noisecubature Kalman filterevent-triggered mechanismnonlinear systempacket dropoutperformance analysis |
| spellingShingle | Guorui Cheng Jingang Liu Shenmin Song Event-Triggered State Filter Estimation for Nonlinear Systems with Packet Dropout and Correlated Noise Sensors correlated noise cubature Kalman filter event-triggered mechanism nonlinear system packet dropout performance analysis |
| title | Event-Triggered State Filter Estimation for Nonlinear Systems with Packet Dropout and Correlated Noise |
| title_full | Event-Triggered State Filter Estimation for Nonlinear Systems with Packet Dropout and Correlated Noise |
| title_fullStr | Event-Triggered State Filter Estimation for Nonlinear Systems with Packet Dropout and Correlated Noise |
| title_full_unstemmed | Event-Triggered State Filter Estimation for Nonlinear Systems with Packet Dropout and Correlated Noise |
| title_short | Event-Triggered State Filter Estimation for Nonlinear Systems with Packet Dropout and Correlated Noise |
| title_sort | event triggered state filter estimation for nonlinear systems with packet dropout and correlated noise |
| topic | correlated noise cubature Kalman filter event-triggered mechanism nonlinear system packet dropout performance analysis |
| url | https://www.mdpi.com/1424-8220/24/3/769 |
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