Distributed Consensus Filtering of 3D Stochastic Nonlinear Parabolic Systems Over Mobile Sensor Networks

Abstract

This paper focuses on distributed consensus filtering of nonlinear parabolic systems with stochastic perturbations in three-dimensional(3D) space. To obtain consensus estimation, distributed filters is designed through mobile sensor networks. With mobile sensing approach, a optimal framework for the performance enhancement of the parabolic systems. By using operator-dependent Lyapunov functional, sufficient conditions are developed under the filtering error is boundedness. Also, the velocity law of each mobile sensor can be provided as the guidance for the rapid convergence of the filtering error system. Finally, the usefulness of the given method is verified by simulation results.