基于Kullback-Leibler散度的无源传感器数据关联

doi:10.7964/jdxbgxb201306042

摘要/Abstract

摘要：

针对多无源传感器多目标多维分配数据关联模型在构造关联代价时未考虑位置估计不确定性所引入的误差问题,提出了一种基于Kullback-Leibler散度的关联算法。将伪量测信息的概率密度函数与真实观测数据的最大后验概率密度之间的Kullback-Leibler散度作为关联代价,给出了一种Kullback-Leibler散度的近似解析公式,从而简化了计算。最后对经典关联算法与本文算法的正确率进行了试验对比,仿真结果表明:基于Kullback-Leibler散度的关联代价能够更精准地反映出数据关联的可能性,所提关联算法可以获得更好的关联性能。

关键词: 信息处理技术, 数据关联, 多无源传感器, Kullback-Leibler散度

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

中图分类号:

TN953

鹿传国, 冯新喜, 孔云波, 张迪. 基于Kullback-Leibler散度的无源传感器数据关联[J]. 吉林大学学报(工学版), 2013, 43(06): 1696-1701.

LU Chuan-guo, FENG Xin-xi, KONG Yun-bo, ZHANG Di. Data association for multi-passive-sensor system based on Kullback-Leibler divergence[J]. 吉林大学学报(工学版), 2013, 43(06): 1696-1701.

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