基于多目标多特征信息融合数据关联的无源跟踪方法

在多目标无源跟踪中,传统的数据关联方法只利用那些与目标状态向量计算直接相关的信息(如DOA、TOA信息等).本文提出了一种新的数据关联算法——基于多目标多特征信息融合的数据关联算法,该算法同时利用了更多的目标特征信息(如频率、PRI等),应用D-S证据理论进行单目标多特征信息融合,在此基础上,再进行多目标综合数据关联.它是一种基于多特征信息的全局最优的算法.计算机仿真表明,基于该算法的无源跟踪性能要优于传统的NN方法和扩展的NN方法.

Passive Tracking Based on Data Association with Information Fusion of Multi-Feature and Multi-Target

A new data association algorithm based on information fusion of multi-feature and multi-target in passive tracking is proposed in this paper. It uses more features of the target such as the frequency, PRI, while the traditional algorithms only use the features directly correlative with the target state such as DOA, TOA. Based on the information fusion of multiple features with DS evidence theory, the decision of synthetic data association of all the targets is made. With the help of computer simulation, it is proven that the proposed algorithm is superior to the NN method and the expanded NN method.