Information-Driven sensor selection algorithm for kalman filtering in sensor networks

  • Authors:

  • Yu Liu;Yumei Wang;Lin Zhang;Chan-hyun Youn

  • Affiliations:

  • School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China;School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China;School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China;Information and Communications University, Republic of Korea

  • Venue:
  • UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
  • Year:
  • 2006

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Abstract

In this paper, an information-driven sensor selection algorithm is proposed to select sensors to participate in Kalman filtering for target state estimation in sensor networks. The mutual information between the measurements of sensors and the estimated distribution of the target state is considered as the information utility function to evaluate the information contribution of sensors. And only those sensors with larger mutual information are selected to participate in the Kalman filtering iterations. Then the geographic routing mechanism is utilized to visit these selected sensors sequentially and set up a path to transport the state estimation information to the sink node. Simulation results show that compared with the shortest path tree algorithm, the information-driven sensor selection algorithm involves smaller participated sensors, and shorter total communication distance, while the estimation performance approaches the same bound.