Distributed resilient filtering for time-varying systems over sensor networks subject to Round-Robin/stochastic...

Area of Science:

• Control Systems Engineering
• Networked Systems
• Signal Processing

Background:

• Distributed filtering is crucial for sensor networks, but faces challenges from communication constraints and parameter uncertainties.
• Existing methods often struggle with the complexities of nonlinear time-varying systems and strict communication protocols like Round-Robin/stochastic.
• Ensuring resilient state estimation under these conditions is vital for reliable network operation.

Purpose of the Study:

• To design a distributed resilient filter for nonlinear time-varying systems in sensor networks.
• To address the impact of communication protocol constraints (Round-Robin/stochastic) on data transmission and state estimation.
• To develop a filter that suppresses adverse effects from imprecise filter parameters.

Main Methods:

• Utilizing a stochastic analysis approach to model system behavior under protocol constraints.
• Employing recursive linear matrix inequality (LMI) techniques for filter design and analysis.
• Establishing sufficient conditions to guarantee H-infinity performance for the state estimation error system.

Main Results:

• Sufficient conditions were derived to ensure the state estimation error system meets H-infinity performance constraints.
• The proposed filter design effectively suppresses the impact of parameter imprecision.
• The methodology accounts for the limitations imposed by Round-Robin/stochastic communication protocols.

Conclusions:

• The developed distributed resilient filter is effective for nonlinear time-varying systems over sensor networks.
• The approach successfully integrates communication protocol constraints and parameter uncertainties.
• Numerical simulations confirm the feasibility and effectiveness of the proposed filtering strategy.