基于信息散度的雷达/红外数据关联算法

针对基于多维分配模型的雷达与红外数据关联算法在构造关联代价函数时未考虑最大似然估计引入的误差的一问题,提出了一种基于信息散度的雷达与红外数据关联算法.该算法首先利用无迹变换获得伪量测的统计信息.然后在构造关联代价函数时,将真实量测数据的极大后验分布和伪量测的概率密度函数的之间的Kullback-Leibler散度(KLD)作为关联代价,继而代入多维分配模型求解关联.最后进行了仿真分析,结果表明该算法具有良好的关联性能,其关联代价可更精准地反映数据关联的可能性程度.

Data association algorithm for radar and infrared sensor based on information divergence

The cost function of the current multi-target data association algorithms based on the S-D assignment for heterogeneous sensors is computed by using maximum likelihood estimation of the target position without taking the estimation errors into account. To overcome the problem above, a new data association algorithm was proposed based on the information divergence,which first used the unscented transform to get the statistic information of the pseudo measurements,then the differentia between the probability density function of pseudo measurements and the most posterior probability density function worked as the association cost. Finally simulation experiments were made to validate the proposed algorithm. The results show that the proposed algorithm can achieve better performance and its association cost reflects the association probability more accurately.