Finite-Time Distributed State Estimation Over Sensor Networks With Round-Robin Protocol and Fading Channels

Area of Science:

• Control Systems Engineering
• Networked Systems
• Nonlinear System Analysis

Background:

• Distributed state estimation in sensor networks faces challenges from limited channel capacity and multiplicative noise.
• Existing methods may not adequately address performance degradation under resource constraints and channel fading.

Purpose of the Study:

• To develop a finite-time distributed state estimation strategy for discrete-time nonlinear systems over sensor networks.
• To address channel capacity constraints and multiplicative noise using a Round-Robin protocol and resource allocation.
• To ensure average stochastic finite-time boundedness and stability of the estimation error.

Main Methods:

• Introduction of the Round-Robin protocol to manage sensor node communication and channel capacity.
• Modeling of channel fading using multiplicative noise with varying stochastic properties.
• Application of periodic system analysis and Lyapunov methods for deriving stability conditions.
• Design of estimator gains using the linear matrix inequality (LMI) approach.

Main Results:

• Sufficient conditions for average stochastic finite-time boundedness of the estimation error system were derived.
• Sufficient conditions for average stochastic finite-time stability of the estimation error system were established.
• The proposed estimator gains were designed effectively using the LMI approach.

Conclusions:

• The developed finite-time distributed state estimation approach effectively handles channel capacity constraints and fading.
• The method ensures improved performance and stability for nonlinear systems in networked environments.
• Numerical examples validate the effectiveness of the proposed estimation strategy.