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

TL;DR

This paper develops a delay-dependent distributed Kalman fusion estimation method with dimensionality reduction for cyber-physical systems, ensuring stability and reduced computational complexity despite communication delays.

Contribution

It introduces a novel model with compensation for delays and dimensionality reduction, and derives a stable, steady-state distributed Kalman fusion estimator with lower complexity.

Findings

The proposed estimator guarantees convergence of the estimation error covariance.

The steady-state estimator reduces computational complexity significantly.

Examples demonstrate the effectiveness and advantages of the method.

Abstract

This paper studies the distributed dimensionality reduction fusion estimation problem with communication delays for a class of cyber-physical systems (CPSs). The raw measurements are preprocessed in each sink node to obtain the local optimal estimate (LOE) of a CPS, and the compressed LOE under dimensionality reduction encounters with communication delays during the transmission. Under this case, a mathematical model with compensation strategy is proposed to characterize the dimensionality reduction and communication delays. This model also has the property to reduce the information loss caused by the dimensionality reduction and delays. Based on this model, a recursive distributed Kalman fusion estimator (DKFE) is derived by optimal weighted fusion criterion in the linear minimum variance sense. A stability condition for the DKFE, which can be easily verified by the exiting software, is derived. In addition, this condition can guarantee that estimation error covariance matrix of the DKFE converges to the unique steady-state matrix for any initial values, and thus the steady-state DKFE (SDKFE) is given. Notice that the computational complexity of the SDKFE is much lower than that of the DKFE. Moreover, a probability selection criterion for determining the dimensionality reduction strategy is also presented to guarantee the stability of the DKFE. Two illustrative examples are given to show the advantage and effectiveness of the proposed methods.