Particle filter method for a centralized multisensor system

  • Authors:

  • Wei Xiong;You He;Jing-wei Zhang

  • Affiliations:

  • Research Institute of Information Fusion, Naval Aeronautical Engineering Institute, Yantai, China;Research Institute of Information Fusion, Naval Aeronautical Engineering Institute, Yantai, China;Research Institute of Information Fusion, Naval Aeronautical Engineering Institute, Yantai, China

  • Venue:
  • ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
  • Year:
  • 2005

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Abstract

Multisensor state estimation is an important issue in multisensor data fusion. In order to solve the centralized multisensor state estimation problem of a non-Gaussian nonlinear system, the paper proposes a new multisensor sequential particle filter (MSPF). First, the general theoretical model of a centralized multisensor particle filter is obtained. Then, a sequential resampling method is proposed according to the characteristics of a centralized multisensor system. Last, a Monte Carlo simulation is used to analyze the performance of the method. The results of the simulation show that the new method can greatly improve the state estimation precision of a multisensor system. Moreover, it will gain more accuracy in estimation with an increase in sensor numbers.