Locally Minimum-Variance Filtering of 2-D Systems Over Sensor Networks With Measurement Degradations: A Distributed Recursive Algorithm
Fan Wang, Zidong Wang, Jinling Liang, Jun Yang
Abstract
This article tackles the recursive filtering problem for an array of 2-D systems over sensor networks with a given topology. Both the measurement degradations of the network outputs and the stochastic perturbations of network couplings are modeled to reflect engineering practice by introducing some random variables with given statistics. The goal of the addressed problem is to devise the distributed recursive filters capable of cooperatively estimating the true state in order to ensure locally minimal upper bound (UB) on the second-order moment of the filtering error (also viewed as the general error variance). For this purpose, the general error variance regarding the underlying target plant is first provided to facilitate the subsequent filter design, and then a certain UB on the error variance is constructed by exploiting the stochastic analysis and the induction approach. Furthermore, in view of the inherent sparsity of the sensor network, the gain parameters of the desired distributed filters are determined, and the proposed recursive filtering algorithm is shown to be scalable. Finally, an illustrative example is given to demonstrate the validity of the established filtering strategy.