Recursive Minimum-Variance Filter Design for State-Saturated Complex Networks With Uncertain Coupling Strengths...

Area of Science:

• Control Systems Engineering
• Network Science
• Signal Processing

Background:

• Complex networks (CNs) are susceptible to state saturation and uncertain coupling strengths (UCSs).
• Deception attacks pose a significant threat to measurement signals in communication networks, often modeled by Bernoulli processes.
• Robust state estimation is critical for the reliable operation of CNs under such challenging conditions.

Purpose of the Study:

• To design a minimum-variance recursive filter for state-saturated CNs with UCSs and deception attacks.
• To guarantee upper bounds on the error covariances of the filtering error.
• To ensure the exponential mean-square boundedness of filtering errors.

Main Methods:

• Utilizing a recursive filtering approach to handle the dynamic nature of CNs.
• Developing a filter design that explicitly accounts for state saturation and UCSs.
• Incorporating a stochastic model for deception attacks (Bernoulli distribution).
• Employing optimization techniques to minimize the trace of error covariance upper bounds for filter gain calculation.

Main Results:

• Sufficient conditions are derived to ensure the exponential mean-square boundedness of filtering errors.
• The proposed filter effectively guarantees upper bounds on error covariances.
• Simulation examples, including a practical application, demonstrate the approach's effectiveness.

Conclusions:

• The developed recursive filter provides a robust solution for state estimation in complex networks facing saturation, uncertainties, and deception attacks.
• The method ensures performance guarantees in terms of error covariance bounds and stability.
• The findings are validated through simulations, highlighting practical applicability.