无源跟踪中基于灰关联信息融合的 概率数据关联算法

在无源跟踪中,传统的概率数据关联算法(PDA)单纯依据状态量测信息.该文提出了一种基于多特征信息融合的概率数据关联算法,该算法利用目标多个特征信息(状态量测、载频、PRI等),通过灰关联分析的方法,计算有效观测与目标的关联概率,进而在信息融合的基础上,进行目标状态更新估计.该算法与传统的PDA相比有两个优点,第一,关联概率的计算量小、计算更准确;第二,该算法利用了目标多特征信息,因此,关联性能更好.实验表明,基于该算法的无源跟踪性能明显优于NN方法和传统的PDA方法.

Probabilistic Data Association Algorithm of Passive Tracking Based on Information Fusion with Gray Correlation Analysis

Traditional Probabilistic Data Association(PDA) algorithm only use the features directly correlative with the target state.A new PDA algorithm based on information fusion of multi-feature of the emitter in passive tracking is proposed.First,the association probability is calculated with gray correlation analysis of multiple features of the target such as the RF,PRI,and PW.Further,the data association is made and the target state is updated based on the information fusion.Compared with the traditional PDA algorithm,the proposed algorithm has two advantages.One is that the calculation of the association probability is faster and more accurate,and the other is that the association performance is much better.Experiments show that the proposed algorithm is superior to the NN algorithm and the traditional PDA algorithm.