Distributed Extended Kalman Consensus Filtering for Multi-Sensor Networked Nonlinear Systems Under Stochastic Communication Protocol

Abstract

The distributed extended Kalman consensus filtering problem under stochastic communication protocol (SCP) is investigated for multi-sensor networked nonlinear systems. In the sensor network, each sensor node and its neighbor nodes occupy a limited number of communication channels when exchanging measurement data. Utilizing the SCP-equipped communication network ensures that the neighbor nodes of each sensor node randomly access these channels and send measurement data based on the number of channels at each step. A set of random variables is introduced to represent the neighbor nodes whose measurements are selected for transmission at each step. When each sensor node is aware of the measurement data received from its neighbor nodes at each step, a distributed extended Kalman consensus filter dependent on random variables is designed. To improve the estimation consensus among nodes, a consensus term is added to the performance metric, and a weighting factor is introduced to assign weight between estimation accuracy and consensus. The optimal filtering gain is derived by minimizing this performance metric. A sufficient condition for the exponential mean-square boundedness of the filtering error is given. Finally, the proposed algorithm’s effectiveness is validated through a simulation example.