多目标无源跟踪中的多特征模糊综合数据关联算法

Data Association Algorithm Based on Fuzzy Synthetic Evaluation of Multiple Features in Multi-Target Passive Tracking

提出了一种多目标无源跟踪中的数据关联算法———多特征模糊综合数据关联算法。首先基于目标特征关联因子模型,进行单跟踪门的多特征数据关联模糊评估,得到单跟踪门内的有效观测与真实目标的模糊关联程度,在此基础上,进行了多目标数据关联模糊综合评估,得到多目标整体模糊关联程度,最后进行多目标关联判决。由于该算法综合利用了目标更多的特征信息,所以比传统的NN方法有更好的关联性能,计算机仿真表明,它比NN方法的错误关联率约低15%。

A new data association algorithm based on fuzzy synthetic evaluation of multiple features in multi-target passive tracking is proposed in this paper. Based on the association factor models, the fuzzy association degree of all the effective observations within the single tracking gate is calculated by fuzzy evaluation of multiple features. Further, the fuzzy association degree of all the targets is calculated by fuzzy synthetic evaluation. At last, the decision of data association is made. Because of the utilization of more features of the target, the proposed algorithm is superior to the NN method, and the association error ratio is decreased about 15%, which is proven with the help of computer simulation.