Distributed Estimation by Partial Sensor Measurements Through Transmission Scheduling for Stochastic Systems

This paper is concerned with the partial-sensor-measurements-based (PSMB) distributed estimation problem for a class of stochastic systems (SSs) with randomly occurring nonlinearities, persistent bounded noises and quantization effects. The observations of partial sensor nodes are available to be transmitted to the estimators. In order to enhance the utilization efficiency of limited resources, the Round-Robin protocol is deployed to schedule the data transmissions over communication networks. The sufficient condition is established to guarantee the mean-square exponential ultimate boundedness of the augmented estimation error system (AEES), and then the desired PSMB estimator gains are determined by minimizing the mean-square upper bound of the augmented estimation error vector subject to iterative matrix inequalities. Finally, an illustrative example demonstrates the effectiveness of proposed estimation scheme.