吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (06): 1696-1701.doi: 10.7964/jdxbgxb201306042

• paper • Previous Articles     Next Articles

Data association for multi-passive-sensor system based on Kullback-Leibler divergence

LU Chuan-guo1, FENG Xin-xi1, KONG Yun-bo1, ZHANG Di2,3   

  1. 1. Information and Navigation Institute, Airforce Engineering University, Xi'an 710077, China;
    2. Equipment Command and Control Department, Academy of Armed Force Engineering, Beijing 100072, China;
    3. Chinese aviation Museum, Beijing 102211, China
  • Received:2012-07-19 Online:2013-11-01 Published:2013-11-01

Abstract:

Traditional multi-dimensional assignment data association algorithm for multi-passive-sensor system ignores the random errors introduced by least square estimation. To overcome such problem, a new data association algorithm based on the Kullback-Leibler divergence is proposed. The Kullback-Leibler divergence between the probability density function of pseudo measurements and the most posterior probability density function works as the association cost. Meanwhile, an analytical form for kullback-Leibler divergence is calculated to alleviate the computation. The correct data association ratios of the traditional algorithm and the proposed one are compared by simulation experiments. The results show that the Kullback-Leibler divergence reflects the association probability more accurately and the proposed algorithm can achieve better performance.

Key words: information processing, data association, multi-passive-sensor, Kullback-Leibler divergence

CLC Number: 

  • TN953

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