Reputation-Aware Distributed Filtering for Nonlinear Bias-Corrupted Systems Over Sensor Networks Under...

Area of Science:

• Control Systems Engineering
• Signal Processing
• Networked Systems

Background:

• Distributed filtering is crucial for networked systems but faces challenges from nonlinearities, unknown inputs (dynamical bias), and data transmission issues.
• Event-triggered mechanisms (ETMs) reduce data transmission load but require careful design to avoid data conflicts and network congestion.
• Reputation mechanisms can improve data reliability in distributed systems by identifying and mitigating abnormal data from neighbors.

Purpose of the Study:

• To develop a reputation-aware Kalman-type distributed filtering (RAKTDF) algorithm for nonlinear bias-corrupted systems (NBCSs).
• To incorporate a dynamical event-triggered mechanism (DETM) for efficient data transmission.
• To enhance filtering accuracy by modeling and utilizing a trust-based reputation mechanism.

Main Methods:

• A novel dynamical bias is introduced and modeled with a dynamical equation.
• A DETM is employed to regulate data transmission frequency, preventing network congestion.
• A trust-based reputation mechanism is developed to score and filter abnormal data from neighboring nodes.
• The RAKTDF algorithm is recursively derived, determining filter gain by minimizing the covariance upper bound of the filtering error dynamic (CUBFED).

Main Results:

• The covariance upper bound of the filtering error dynamic (CUBFED) is derived.
• A sufficient condition for the boundedness of the filtering error dynamics is established.
• The proposed RAKTDF algorithm demonstrates effectiveness in improving filtering accuracy in the presence of nonlinearities and bias.

Conclusions:

• The developed RAKTDF algorithm effectively addresses the filtering problem in nonlinear bias-corrupted systems with a DETM.
• The reputation-aware mechanism significantly improves filtering accuracy by mitigating the impact of abnormal data.
• The proposed method offers a robust solution for distributed filtering in networked systems with improved data management and reliability.