A mobile sensing approach to distributed consensus filtering of 2D stochastic nonlinear parabolic systems with disturbances

Abstract

The focus of this paper is on distributed consensus filtering in two-dimensional (2D) space for stochastic nonlinear parabolic systems with disturbances. Distributed filters are used in mobile sensor networks to achieve consensus estimate. By utilizing a mobile sensing approach, the optimal framework for parabolic systems to improve filtering performance is provided. Sufficient conditions are created under the filtering error is boundedness by employing operator-dependent Lyapunov functional. The velocity law of each mobile sensor can also be used to guide the filtering error system toward rapid convergence. Finally, numerical examples are used to verify the effectiveness of the suggested approach.

Keywords

• Distributed filtering
• consensus performance
• mobile sensing approach
• stochastic nonlinear parabolic systems
• sector bounded disturbances
• 2D systems