基于区间数聚类的目标分群算法

doi:10.12305/j.issn.1001-506X.2022.02.27

摘要/Abstract

摘要：

针对态势认知中目标数量多、信息不确定、数据不精确等问题, 提出一种基于区间数聚类的目标分群算法。首先, 考虑到传感器测量数据具有误差且数据不完全等因素, 采用区间数对传感器探测到的目标进行特征描述。然后, 为有效利用区间数信息定义了一种新的距离度量, 并给出了改进的区间数聚类目标分群算法。最后, 构造4类相互独立的区间数据集, 对区间数据进行分类测试, 并通过典型想定场景设定多类目标实体, 基于目标空间位置、运动特征和属性等要素进行空间分群和任务分群。仿真结果验证了算法能够有效对目标进行分群, 具有较强的稳定性。

关键词: 目标分群, 区间数, 聚类, 态势认知

Abstract:

In order to solve the problems of large number of targets and uncertain and inaccurate information in situation, a target grouping algorithm based on interval number clustering is proposed. Firstly, aiming at the problem of sensor measurement data with error and incomplete, the interval number is used to describe the characteristics of the target detected by the sensor. Then, a new distance measure is defined to make use of interval number information effectively, and an improved interval number clustering target grouping algorithm is given. Finally, four types of independent interval data sets are constructed to classify and test the interval data. Through typical scenario, multiple types of target entities are set, and spatial groupring and task grouping are carried out based on the elements such as target spatial location, motion characteristics and attributes. Simulation results show that the algorithm can effectively group targets and has strong stability.

Key words: target grouping, interval number, clustering, situational cognitive

中图分类号:

TN957

王海滨, 关欣, 衣晓. 基于区间数聚类的目标分群算法[J]. 系统工程与电子技术, 2022, 44(2): 577-583.

Haibin WANG, Xin GUAN, Xiao YI. Method of target grouping based on interval number clustering[J]. Systems Engineering and Electronics, 2022, 44(2): 577-583.

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r₁、r₂范围	IDCWKM	K-means++	K-means
[1, 5]	0.956 0	0.842 7	0.762 3
[1, 10]	0.955 3	0.831 6	0.753 2
[1, 15]	0.953 1	0.829 5	0.757 2
[1, 20]	0.952 2	0.823 7	0.613 5

目标编号	X/m	Y/m	Z/m	速度/(km/h)	航向/(°)	雷达截面积/m²	通信频率/MHz	空间群	任务群
T1	[4 217, 4 706]	[4 639, 5 090]	[19, 21]	[38, 61]	[19, 24]	[397, 411]	[364, 369]	S1	A1
T2	[4 578, 5 122]	[5 236, 5 390]	[11, 16]	[61, 74]	[26, 38]	[284, 421]	[355, 412]	S1	A1
T3	[3 583, 3 661]	[5 369, 5 673]	[14, 16]	[39, 70]	[13, 45]	[415, 426]	[420, 508]	S1	A1
T19	[5 757, 7 025]	[4 794, 5 780]	[3 170, 3 500]	[597, 761]	[103, 119]	[12, 14]	[288, 328]	S2	A1
T20	[5 633, 7 168]	[4 886, 5 644]	[3 226, 3 314]	[683, 842]	[82, 121]	[13, 15]	[422, 481]	S2	A1
T31	[1 369, 2 075]	[3 743, 3 820]	[15, 18]	[40, 50]	[179, 185]	[369, 428]	[367, 457]	S3	A2
T32	[280, 314]	[4 756, 4 812]	[14, 16]	[60, 74]	[172, 199]	[326, 414]	[380, 469]	S3	A2
T43	[8 034, 9 260]	[4 680, 5 180]	[6 615, 6 761]	[880, 967]	[246, 267]	[15, 17]	[346, 406]	S4	A2
T44	[8 002, 9 274]	[3 669, 4 515]	[6 931, 7 174]	[758, 796]	[251, 271]	[15, 23]	[375, 423]	S4	A2
T45	[6 839, 7 139]	[5 061, 5 328]	[6 936, 6 995]	[559, 740]	[241, 278]	[12, 16]	[393, 463]	S4	A2

指标	IDCWKM	K-means++	K-means
CR	1	0.853 9	0.488 5
准确率/%	100	92.6	74.2

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