层次聚类的航迹起始算法

Track initial algorithm based on layered clustering method

针对无源侦察数据不存在周期性扫描、目标定位点迹间的时间间隔随机以及目标数量、运动特性等多项先验信息缺乏状况下的多目标检测问题,提出了层次聚类的航迹起始算法。该算法首先利用信号载频、重频、脉宽参数体制的不同对量测记录集进行粗聚类;其次对雷达工作体制相同的每一个子类,采用K-Means算法对其载频、重频、脉宽三个信号参数进行精聚类;再次对属性聚类后的每一个子类构造所有可能的配对点迹,并计算其分维速度,利用速度法筛选出满足速度约束条件的点迹;最后对筛选出的点迹按接收时间重新排序,利用扩展的搜索算法从第一个时刻开始搜索目标航迹。仿真与真实数据的实验结果验证了本文算法的有效性和实用性。

A new track initial algorithm based on layered clustering method is proposed to solve multi-target detection problem with passive reconnaissance data.Especially when the passive reconnaissance scanned aperiodically,the capture plots are fragmentary,and the prior information of targets number and athletic characteristics are insufficient.The algorithm effectively utilized the attributive characteristics to solve the track initial problem.Firstly,the observation set was rough clustered according to the systems of Pulse Frequency（PF）,Pulse Recurrence Frequency（PRF）,Pulse Width（PW） electromagnetic parameter;Secondly,the exact result of classification was get by clustering of electromagnetic parameter using K-Means algorithm;Thirdly,computing the velocity of each dimension of all of the probable point pairs to eliminate the illusive observations by space-time constrained conditions;Ultimately,reset the selected observations according to their capture time,and an extended search approach is utilized to find the final initialed track.Experiments on both simulated and real world data showed its effectiveness and practicability.