基于目标群拓扑特征的飞行器集群相对定位

doi:10.12305/j.issn.1001-506X.2025.11.28

系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (11): 3816-3825.doi: 10.12305/j.issn.1001-506X.2025.11.28

• 制导、导航与控制 • 上一篇

基于目标群拓扑特征的飞行器集群相对定位

牟新刚¹, 彭文凯¹, 管叙军²^,³^,*, 付文兴³, 郝明瑞³

1. 武汉理工大学机电工程学院，湖北武汉 430070
2. 北京航空航天大学自动化科学与电气工程学院，北京 100191
3. 复杂系统控制与智能协同全国重点实验室，北京 100074

收稿日期:2024-12-18 出版日期:2025-11-25 发布日期:2025-12-08
通讯作者: 管叙军
作者简介:牟新刚（1982—），男，教授，博士，主要研究方向为红外成像、目标检测与跟踪、集群协同探测
彭文凯（2000—），男，硕士研究生，主要研究方向为协同定位、数据融合、多目标跟踪
付文兴（1979—），男，研究员，硕士，主要研究方向为匹配定位、多源融合
郝明瑞（1985—），男，研究员，博士，主要研究方向为智能决策、协同控制
基金资助:
国家自然科学基金(62475200)资助课题

Relative positioning of aircraft cluster based on topological features of target group

Xingang MOU¹, Wenkai PENG¹, Xujun GUAN²^,³^,*, Wenxing FU³, Mingrui HAO³

1. School of Mechanical and Electronic Engineering，Wuhan University of Technology，Wuhan 430070，China
2. School of Automation Science and Electrical Engineering，Beihang University，Beijing 100191，China
3. National Key Laboratory of Complex System Control and Intelligent Agent Cooperation，Beijing 100074，China

Received:2024-12-18 Online:2025-11-25 Published:2025-12-08
Contact: Xujun GUAN

摘要/Abstract

摘要：

飞行器集群自身的高精度定位是执行协同任务的必要前提，针对全球卫星导航系统（Global Navigation Satellite System，GNSS）拒止且环境特征信息受限的多目标协同探测场景，利用集群内数据链仅测距以及对多目标的探测信息，提出一种基于目标群拓扑特征的协同定位方法。通过多维标度（multidimensional scaling，MDS）构建数据链测距多面体，根据目标群三维拓扑特征对目标关联并建立相对定位最优化模型，利用带压缩因子的改进粒子群优化（particle swarm optimization，PSO）算法获取测距多面体姿态的最优解。仿真结果表明，能够获得较高的集群相对定位精度，同时结合惯性导航系统（inertial navigation system，INS）位置输出提升了飞行器集群的绝对位置精度。

关键词: 相对定位, 目标关联, 多维标度, 粒子群优化

Abstract:

High-precision positioning of aircraft clusters is a necessary prerequisite for executing cooperative tasks. Aiming at the scenarios of multi target collaborative detection where the Global Navigation Satellite System (GNSS) is denied and environmental feature information is limited, a cooperative positioning method based on the topological features of target groups is proposed. This method uses only the distance measurements from the data link within the cluster and the multi target detection information. A distance-measurement polyhedron is constructed using multidimensional scaling (MDS), and target association is performed based on the three-dimensional topological features of the target group. A relative positioning optimization model is then established. The optimal solution for the polyhedron’s attitude is obtained by using an improved particle swarm optimization (PSO) algorithm with a compression factor. Simulation results demonstrate that high relative positioning accuracy of the cluster can be achieved. Combined with the inertial navigation system (INS) position output, the absolute positioning accuracy of the aircraft cluster is also improved.

Key words: relative positioning, target association, multidimensional scaling (MDS), particle swarm optimization (PSO)

中图分类号:

TN 96

牟新刚, 彭文凯, 管叙军, 付文兴, 郝明瑞. 基于目标群拓扑特征的飞行器集群相对定位[J]. 系统工程与电子技术, 2025, 47(11): 3816-3825.

Xingang MOU, Wenkai PENG, Xujun GUAN, Wenxing FU, Mingrui HAO. Relative positioning of aircraft cluster based on topological features of target group[J]. Systems Engineering and Electronics, 2025, 47(11): 3816-3825.

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误差项	$\sigma _{\text{yaw }}/(^\circ) $	$\sigma _{\text{pitch}}/(^\circ) $	${\sigma _{\text{range}}}/{\text{m}}$	${\sigma _{{\theta}}}/(^\circ) $	${\sigma _{\phi}} /(^\circ) $	${\sigma _{{\text{dis}}}}/{\mathrm{m}}$
标准差	0.12	0.08	20	0.50	0.50	50

编号	1	2	3	4	5	6	7
1	0.91	0.53	0.72	0.42	0.47	0.56	0.68
2	0.57	0.92	0.54	0.63	0.42	0.48	0.54
3	0.58	0.33	0.91	0.21	0.43	0.50	0.77
4	0.58	0.66	0.35	0.92	0.36	0.46	0.41
5	0.39	0.30	0.53	0.30	0.88	0.70	0.60
6	0.38	0.35	0.44	0.33	0.45	0.81	0.56
7	0.62	0.50	0.85	0.41	0.72	0.64	0.94

视角1下的目标编号	视角2下的关联对象/关联概率
1	1/0.56，4/0.44
2	2/1.00
3	3/0.54，7/0.46
4	4/1.00
5	5/0.56，6/0.44
6	6/1.00
7	3/0.34，5/0.29，7/0.37

算法	${{\overline X_{\text{a}}}} {\text{/m}}$	${S_{\text{a}}}{\text{/m}}$	${{\overline X_{\text{b}}}} $/次	${S_{\text{b}}}$/次	${{\overline X_{\text{c}}}}/ {\text{s}}$	${S_{\text{c}}}/{\text{s}}$
基础PSO	4 171.996	4 624.603	66.780	24.992	0.359	0.134
标准PSO	2 728.829	6 376.173	178.070	31.148	0.954	0.165
压缩因子PSO	1 756.769	4 646.954	120.820	20.487	0.638	0.110
自适应惯性权重的混合PSO	2 569.188	5 966.492	152.740	23.087	0.818	0.122
动态调整惯性权重的PSO	1 798.137	5 259.375	189.900	28.512	1.082	0.162

算法	${{\overline X_{\text{a}}}} /{\text{m}}$	${S_{\text{a}}}/{\text{m}}$	${{\overline X_{\text{b}}}} /次$	${S_{\text{b}}}$/次	${{\overline X_{\text{c}}}} /{\text{s}}$	${S_{\text{c}}}/{\text{s}}$
基础PSO	595.867	396.302	66.475	21.560	0.336	0.128
标准PSO	0.000	0.000	182.170	4.638	0.910	0.115
压缩因子PSO	0.000	0.000	96.395	4.312	0.528	0.077
自适应惯性权重的混合PSO	4.692	57.738	152.115	5.813	0.765	0.111
动态调整惯性权重的PSO	16.905	161.072	196.875	20.046	1.055	0.164

基于目标群拓扑特征的飞行器集群相对定位

Relative positioning of aircraft cluster based on topological features of target group

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摘要/Abstract

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图/表 17

参考文献 30

相关文章 15

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Metrics

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对象	$x$向/km	$y$向/km	$z$向/km
飞行器	[0，5]	[0，5]	[5，10]
目标	[30，35]	[30，35]	[5，10]

参数	分布距离/m	${l_{\text{p}}}$/m
参数	分布距离/m	2500	5000	7500	10000
${l_{\mathrm{t}}}$/m	2500	[1.909，1.847，1.975]	[0.984，0.829，0.947]	[0.604，0.614，0.631]	[0.384，0.376，0.385]
	5000	[2.183，1.981，1.908]	[1.036，0.940，0.959]	[0.659，0.572，0.676]	[0.434，0.405，0.384]
	7500	[2.275，1.800，2.248]	[0.948，0.923，0.970]	[0.591，0.583，0.597]	[0.442，0.398，0.439]
	10000	[2.035，1.726，1.956]	[0.881，0.801，0.869]	[0.649，0.601，0.598]	[0.395，0.367，0.408]

节点	$\Delta x_i^{{{\mathrm{I}}}}$	$ \Delta \tilde x_i^{\mathrm{I}} $	$\Delta y_i^{\mathrm{I}}$	$\Delta \tilde y_i^{\mathrm{I}}$	$\Delta z_i^{\mathrm{H}}$	$ \Delta \tilde z_i^{\mathrm{H}} $
1	−43.650	114.609	1015.283	−87.193	−34.083	−7.096
2	676.378	128.276	−958.789	−81.230	−11.314	−6.544
3	−237.013	125.531	−858.570	−87.963	2.934	13.944
4	700.412	128.895	887.410	−81.721	9.436	−3.688
5	−127.239	121.779	−1123.647	−81.727	28.650	−15.505
6	−232.733	117.065	528.499	−89.980	−7.150	7.363

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节点数量	5	6	7	8
精度提升倍数	3.871	4.064	4.331	4.585