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Event-Triggered State Filter Estimation for Nonlinear Systems with Packet Dropout and Correlated Noise
Guorui Cheng1, Jingang Liu1, Shenmin Song1
1Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel, Switzerland)
|February 10, 2024
Summary
This study introduces an event-triggered state estimation method for nonlinear systems, balancing performance and data transmission by minimizing redundant communication. The approach ensures stability despite packet dropout and correlated noise.
Area of Science:
- Control Systems Engineering
- Signal Processing
- Nonlinear System Analysis
Background:
- State estimation in nonlinear systems is challenged by packet dropout and correlated noise.
- Event-triggered communication strategies aim to reduce data transmission load.
- Reliable state estimation requires robust filtering under network uncertainties.
Purpose of the Study:
- To develop an event-triggered state estimation framework for nonlinear systems with packet dropout.
- To integrate noise decorrelation and unreliable network transmission into the estimator design.
- To analyze the trade-off between estimation performance and communication rate.
Main Methods:
- An event-triggered communication mechanism based on condition violation.
- Noise decorrelation techniques applied prior to filter design.
- Integration of the event-triggered mechanism and network transmission for state estimation.
- Utilizing the three-degree spherical-radial cubature rule for numerical implementation.
Main Results:
- Adjusting the event-triggered threshold balances estimation performance and transmission rate.
- The covariance matrix of the state estimation error remains bounded with a lower bound on packet dropout rate.
- Stochastic stability of the state estimation error is confirmed.
Conclusions:
- The proposed event-triggered state estimation algorithm is effective for nonlinear systems.
- The method provides a balance between estimation accuracy and communication efficiency.
- Validation through a target tracking system simulation confirms the algorithm's efficacy.
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