Sensor-Based Filtering of Stochastic Distributed Parameter Systems

Abstract

This Paper Proposes a Scheme for Filtering of Stochastic Distributed Parameter Systems. it is Assumed that a Real-Time Environment Consists of m Groups of Sensors, each of which Provides Necessarily State Spatially Measurements from Sensing Devices. Base on Lyapunov Stability Theorem and Itô formula, a Class of Distributed Adaptive Filters with Penalty Terms Result in the State Errors Forming a Stable Evolution System and Asymptotically Converge to Stochastic Distributed Parameter Systems, and then the Preferable State Estimation is Derived. Numerical Simulation Demonstrates the Effectiveness of the Proposed Method.