IJCAS

In this study, a novel distributed Kalman filter based on a possibilistic framework was proposed to mitigate fuzzy noisein nonlinear multiagent systems. To describe fuzzy uncertainty, noises were modeled as fuzzy random variables with trapezoidal probability distributions instead of Gaussian distributions. A fuzzy information fusion (FIF) algorithm was proposed to fuse fuzzy state estimations from neighboring nodes. The nonlinear problem was solved by using local linearization. A distributed extended fuzzy information filter was designed by combining the FIF algorithm and local linearization in distributed sensor networks. The stability of this filter was analyzed. Finally, a target tracking simulation was performed to detail the effectiveness of the proposed filter algorithm.

Keywords: Consensus, distributed filtering, extended Kalman filtering, fuzzy uncertainty.