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

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 KullbackLeibler 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.