多机编队已成为未来空战的主要作战模式,编队内成员距离近,相互遮蔽,导致检测不连续,在给定雷达工作带宽前提下如何对编队目标进行有效分辨,实现连续稳定跟踪已成为亟待解决的难题。针对该问题,文中采用均值漂移(MeanShift)聚类算法对测量数据进行编队中心计算,提取聚类信息;根据聚类结果自适应采用删除均值恒虚警率检测算法提升编队内目标的检测能力;通过统计历史测量数据的直方图分布,估计目标检测概率,采取编队中心维持跟踪策略,根据检测概率高低自适应完成编队中心跟踪与个体跟踪的自适应切换,保证整体态势清晰前提下能够实现个体目标的准确跟踪。仿真实验表明,所提算法大大增加可分辨时间,分辨能力提升25??,编队目标稳定跟踪,全程无混批,为密集编队目标的检测跟踪提供了一种新的解决思路。

Aircrafts in formation has become main combat mode in future air war. Memberships in formation are spaced closely and mutual overshadowed, which results in discontinuity in detection. Hence, how to resolve the formation targets and continually tracking under a presented radar working band has become an urgent problem. To solve this problem, this paper first induce the mean shift clustering algorithm to calculate the formation center using detection data and extract the clustering, then we adaptively adopts the censored mean level detector constant false alarm ratio (CMLD-CFAR) detection algorithm to improve the detection ability for formation targets. Lastly, by using the measurements, we obtain the histogram distribution and estimate the detection probability. Then, the formation center is used to maintain tracking. The algorithm adaptively chooses the tracking result between formation center and individual target according to the detection probability, which can make the situation clear and ensure individual target′s tracking precision. Simulation results show that the proposed algorithm greatly increases the resolving time and the resolving ability is up to 25??. Targets in formation are consecutively tracked without batch confusion, which provides a new detecting and tracking method for dense targets in formation.

TN953