Journal of South China University of Technology (Natural Science Edition) ›› 2015, Vol. 43 ›› Issue (1): 34-40.doi: 10.3969/j.issn.1000-565X.2015.01.006

• Electronics, Communication & Automation Technology • Previous Articles     Next Articles

Situation Assessment Algorithm for Online Parameters Learning in Dynamic Bayesian Networks

Yuan De - ping1 Zheng Juan - yi2 Shi Hao - shan1   

  1. 1. School ofElectronicsInformation ， Northwestern Polytechnical University ， Xi ’ an 710129 ， Shaanxi ， China ； 2. School ofTelecommu-nication and Information Engineering ， Xi ’ an University of Posts and Telecommunications ， Xi ’ an 710061 ， Shaanxi ， China 
  • Received:2014-08-13 Revised:2014-10-17 Online:2015-01-25 Published:2014-12-01
  • Contact: 袁德平（1972-) ，男，博士生，高级工程师，主要从事信息融合、目标跟踪研究. E-mail:depingy@sina.com
  • About author:袁德平（1972-) ，男，博士生，高级工程师，主要从事信息融合、目标跟踪研究.
  • Supported by:
    Supported by the National Natural Science Foundation of China （ 61201194 ） and the Science and Technology Research Development Project of Shaanxi Province （ 2013K06-07)

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Abstract:

In order to assess battlefield situation accurately and quickly ， an algorithm for online parameters learning is proposed on the basis of dynamitic Bayesian networks （ DBN ） . Forward recursion algorithm is used to estimate the parameters of network model after the structure model of dynamic Bayesian network is confirmed by expert knowledge. Dirichlet distribution is used as the prior distribution of samples according to the characteristics of small samples for the observation value of battlefield situation model ， and moment estimation is adopt to estimate the hyper parameters of the prior distribution. Then ， in combination with the equivalent samples value from the prior distribution ， the observation value can be used to implement parameters learning and battlefield situation assessment. Simulated results indicate that the proposed algorithm is of good real-time performance and high accuracy for situation assessment.

Key words: Bayesian networks, situation assessment, Dirichlet distribution, parameters learning

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