Delay-Dependent Distributed Kalman Fusion Estimation With Dimensionality Reduction in Cyber-Physical Systems

Area of Science:

• Cyber-Physical Systems (CPSs)
• Distributed Estimation
• Signal Processing

Background:

• Cyber-physical systems (CPSs) face challenges in distributed estimation due to dimensionality reduction and communication delays.
• Existing methods often struggle to mitigate information loss caused by these factors.
• Accurate state estimation is crucial for the reliable operation of CPSs.

Purpose of the Study:

• To develop a distributed dimensionality reduction fusion estimation method for CPSs.
• To address the impact of communication delays on estimation accuracy.
• To propose a compensation strategy that minimizes information loss.

Main Methods:

• A mathematical model incorporating dimensionality reduction and communication delays with a compensation strategy was developed.
• A recursive distributed Kalman fusion estimator (DKFE) was derived using optimal weighted fusion.
• A stability condition for the DKFE was established, leading to a steady-state DKFE (SDKFE).

Main Results:

• The proposed model effectively reduces information loss from dimensionality reduction and delays.
• The DKFE ensures stability, with its estimation error covariance converging to a unique steady-state matrix.
• The SDKFE offers significantly lower computational complexity compared to the DKFE.

Conclusions:

• The developed DKFE and SDKFE provide effective solutions for distributed estimation in CPSs with communication delays.
• A probability selection criterion guarantees the stability of the DKFE under dimensionality reduction.
• The proposed methods demonstrate significant advantages and effectiveness through illustrative examples.