Distributed consensus filtering for jump Markov linear systems

Authors:
Open Access
Publisher website
Google Scholar
Abstract
This article studies the problem of distributed filtering for jump Markov linear systems in a not fully connected sensor network. A distributed consensus filter is developed by applying an improved interacting multiple model approach in which the mode‐conditioned estimates are derived by the Kalman consensus filter and the mode probabilities are obtained in the sense of linear minimum variance. A numerical example is provided to demonstrate the effectiveness of the proposed algorithm for tracking a manoeuvring target in a sensor work with eight nodes.
Funding Information
  • National Natural Science Foundation of China (61203044, 61134005, 90916024, 61104011, 91116016, 60921001)
  • Natural Science Foundation of Beijing Municipality (4132040, 4122046)

This publication has 22 references indexed in Scilit: