Variance-Constrained Recursive State Estimation for Time-Varying Complex Networks With Quantized Measurements and...

Area of Science:

• Control Systems Engineering
• Network Science
• Stochastic Systems

Background:

• Complex networks are increasingly used in various applications, but their analysis is challenged by time-varying parameters and signal uncertainties.
• State estimation in discrete stochastic systems is crucial for monitoring and control, especially with imperfect measurements.
• Existing methods often struggle with uncertain coupling strengths and the effects of signal quantization.

Purpose of the Study:

• To develop a novel recursive state estimation algorithm for discrete time-varying stochastic complex networks.
• To address challenges posed by uncertain inner coupling strengths and signal quantization.
• To ensure error-variance constraints are met, minimizing estimation error covariance at each sampling instant.

Main Methods:

• A recursive state estimation algorithm is designed incorporating error-variance constraints.
• The algorithm accounts for time-varying coupling strengths within specified intervals.
• Analysis of estimation error boundedness is performed, establishing criteria for exponential boundedness in the mean square sense.

Main Results:

• A new variance-constrained state estimation algorithm is successfully developed.
• The algorithm guarantees a locally minimized upper bound on the estimation error covariance.
• Sufficient conditions for the exponential boundedness of the state estimation error are established.

Conclusions:

• The proposed variance-constrained estimation method is effective for discrete time-varying stochastic complex networks.
• The method provides robust state estimation despite uncertain couplings and signal quantization.
• Simulation results validate the performance and superiority of the developed estimation technique.