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Distributed Dimensionality Reduction Fusion Estimation with Communication Delays in Cyber-Physical Systems
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