基于视觉协同的蜂群自主导航技术

doi:10.12305/j.issn.1001-506X.2026.04.28

摘要/Abstract

摘要：

针对卫星导航拒止条件下无人机(unmanned aerial vehicle, UAV) 蜂群导航精度与可靠性大大降低的问题，提出一种基于视觉与数据链测距协同导航算法。首先，建立了基于视觉协同的蜂群自主导航模型，并设计了一种基于最大相关熵准则的卡尔曼滤波（maximum correntropy criterion Kalman filter，MCC-KF）的分布式协同导航算法，提高了在非高斯噪声下的协同导航定位精度。然后，针对图像处理过程和数据传输过程中出现的延时问题，提出了一种延迟补偿策略与算法。最后，仿真表明，采用分布式协同导航和延迟补偿策略显著提高了UAV蜂群在卫星拒止环境下的导航精度。

关键词: 无人机蜂群, 自主导航, 延迟补偿, 最大相关熵准则的卡尔曼滤波

Abstract:

Aiming at the problem of greatly reduced accuracy and reliability of unmanned aerial vehicle （UAV） swarm navigation under satellite navigation denial conditions, a cooperative navigation algorithm based on vision and data chain ranging is proposed. Firstly, a swarm autonomous navigation model based on visual cooperation is established, and a distributed cooperative navigation algorithm based on maximum correntropy criterion Kalman filter （MCC-KF） is designed, which improves the cooperative navigation and positioning accuracy under non-Gaussian noise. Then, a delay compensation strategy and algorithm are proposed for the delay problems occurring in the image processing process and data transmission process. Finally, simulation shows that the navigation accuracy of UAV swarm in satellite denial environment is significantly improved by using distributed cooperative navigation and delay compensation strategy.

Key words: unmanned aerial vehicle (UAV) swarm, autonomous navigation, delay compensation, maximum correntropy criterion Kalman filter

中图分类号:

V 249.3

杨志, 郁丰, 林思颖, 周紫君. 基于视觉协同的蜂群自主导航技术[J]. 系统工程与电子技术, 2026, 48(4): 1396-1403.

Zhi YANG, Feng YU, Siying LIN, Zijun ZHOU. Swarm autonomous navigation technology based on visual collaboration[J]. Systems Engineering and Electronics, 2026, 48(4): 1396-1403.

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参考文献 33

1	HINCHEY M G, STERRITT R, ROUFF C. Swarms and swarm intelligence[J]. Computer, 2007, 40 (4): 111- 113.
2	王金龙. 基于MEMS与GPS的微小型航姿系统设计与实现[D]. 哈尔滨: 哈尔滨工程大学, 2018.
	WANG J L. Design and realization of micro attitude heading reference system based on combination of MEMS and GPS[D]. Harbin: Harbin Engineering University, 2018.
3	SIVAKUMAR A. UAV swarm coordination and control for establishing wireless connectivity[D]. Singapore: National University of Singapore, 2011.
4	XIONG F, LI A J, WANG H, et all. An SDN-MQTT based communication system for battlefield UAV swarms[J]. IEEE Communications Magazine, 2019, 57 (8): 41- 47. doi: 10.1109/MCOM.2019.1900291
5	LIN Q Y, WANG X L, WANG Y T. Cooperative formation and obstacle avoidance algorithm for multi-UAV system in 3D environment[C]//Proc. of the Chinese Control Conference, 2018: 6943−6948.
6	MALANDRINO F, ROTTONDI C, CHIASSERINI C F, et al. Multiservice UAVs for emergency tasks in post-disaster scenarios[C]//Proc. of the ACM MobiHoc workshop, 2019: 18−23.
7	RYAN G B, BENJIAMIN E W, ADAM R B. Investigating practical impacts of using single-antenna and dual-antenna GNSS/INS sensors in UAS-LiDAR applications[J]. Sensors, 2021, 21 (16): 5382. doi: 10.3390/s21165382
8	LIU X H, LIU X X, YANG Y, et al. Variational Bayesian-based robust cubature Kalman filter with application on SINS/GPS integrated navigation system[J]. IEEE Sensors Journal, 2022, 22 (1): 489- 500. doi: 10.1109/JSEN.2021.3127191
9	FU H P, CHENG Y M. Switching Gaussian-heavy-tailed distribution based robust Gaussian approximate filter for INS/GNSS integration[J]. Journal of the Franklin Institute, 2022, 359 (16): 870- 894.
10	ZHOU Y K, RAO B, WANG W. UAV Swarm intelligence: recent advances and future trends[J]. IEEE Access, 2020, 8: 183856−183878.
11	HELM S V D, COPPOLA M, MCGUIRE K N, et al. On-board range-based relative localization for micro air vehicles in indoor leader–follower flight[J]. Autonomous Robots, 2020, 44 (3): 415- 441.
12	LI S S, COPPOLA M, DE WAGTER C, et al. An autonomous swarm of micro flying robots with range-based relative localization [EB/OL]. [2025-4-11]. https://arxiv.org/pdf/2003.05853v2.
13	熊骏, 熊智, 于永军, 等. 超宽带测距辅助的无人机近距离相对导航方法[J]. 中国惯性技术学报, 2018, 26 (3): 72- 77.
	XIONG J, XIONG Z, YU Y J, et al. Close relative navigation algorithm for unmanned aerial vehicle aided by UWB relative measurement[J]. Journal of Chinese Inertial Technology, 2018, 26 (3): 72- 77.
14	史晨发, 熊智, 蒋旭, 等. 基于AHRS的无人机集群协同导航方法[EB/OL]. [2025-4-11]. https://doi. org/10.13700/j. bh. 1001-5965.2024. 0343.
	SHI C F, XIONG Z, JIANG X, et al. Cooperative navigation for UAV swarm based on AHRS [EB/OL]. [2025-4-11]. https://doi.org/10.13700/j.bh.1001-5965.2024.0343.
15	HARDY J, STRADER J, GROSS J N, et al. Unmanned aerial vehicle relative navigation in GPS denied environments [C]//Proc. of the IEEE/ION Position, Location and Navigation Symposium, 2016: 344−352.
16	张静, 金志华. 空中平台航姿参考系统的设计[J]. 中国惯性技术学报, 2004, 12 (2): 48- 53.
	ZHANG J, JIN Z H. Design of attitude heading reference system for aerial platform[J]. Journal of Chinese Inertial Technology, 2004, 12 (2): 48- 53.
17	刘建业. 导航系统理论与应用[M]. 西安: 西北工业大学出版社, 2010.
	LIU J Y. Theory and application of navigation systems[M]. Xi’an: Northwestern Polytechnical University Press, 2010.
18	王晓龙, 刘海颖, 王景琪. 基于分层SLAM的空地多智能体协同导航[J]. 系统工程与电子技术, 2020, 42 (1): 166- 171.
	WANG X L, LIU H Y, WANG J Q. Collaborative navigation of air-ground multi-agent based on hierarchical SLAM[J]. Systems Engineering and Electronics, 2020, 42 (1): 166- 171.
19	齐乃新, 张胜修, 曹立佳, 等. 基于辅助匹配的1点RANSAC单目视觉导航算法[J]. 系统工程与电子技术, 2018, 40 (5): 1109- 1117.
	QI N X, ZHANG S X, CAO L J, et al. Monocular visual navigation method with 1-point RANSAC based on aided matching[J]. Systems Engineering and Electronics, 2018, 40 (5): 1109- 1117.
20	周紫君, 郁丰, 吴方, 等. 一种机载惯性/光电组合导航误差修正方法[J]. 中国惯性技术学报, 2024, 32(8): 779−786, 794.
	ZHOU Z J, YU F, WU F, et al. Error correction method for airborne SINS/EODS integrated navigation[J]. Journal of Chinese Inertial Technology, 2024, 32(8): 779−786, 794.
21	CHEN B, LIU X, ZHAO H, et al. Maximum correntropy Kalman filter[J]. Automatica, 2017, 76: 70−77.
22	WANG J Q, LYU D, HE Z, et al. Cauchy kernel-based maximum correntropy Kalman filter[J]. International Journal of Systems Science. 2020, 51(16): 3523−3538.
23	赵建印, 毛廷鎏, 杨根庆, 等. 最大相关熵卡尔曼滤波误差修正方法[J]. 现代防御技术, 2024, 52 (5): 156- 161.
	ZHAO J Y, MAO T L, YANG G Q, et al. Maximum correlation entropy Kalman filter error correction method[J]. Modern Defence Technology, 2024, 52 (5): 156- 161.
24	IZANLOO R, FAKOORIAN S A, YAZDI H S, et al. Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise[C]//Proc. of the Annual Conference on Information Science and Systems, 2016: 500−505.
25	冯忠华, 王新龙, 王彬. 一种数据链传输延迟建模及其补偿方法[J]. 北京航空航天大学学报, 2012, 38 (8): 1106- 1110.
	FENG Z H, WANG X L, WANG B. Transfer time delay model of data link and its compensation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2012, 38 (8): 1106- 1110.
26	刘敏. 数据链测试评估关键技术研究[D]. 西安: 西安电子科技大学, 2009.
	LIU M. Research on key technologies of data link test and evaluation[D]. Xi’an: Xidian University. 2009.
27	周伟超. 基于最大相关熵卡尔曼滤波的组合导航方法[J]. 信息技术, 2022, 46 (9): 78- 83.
	ZHOU W C. Integrated navigation method based on maximum joint entropy Kalman filter[J]. Information Technology, 2022, 46 (9): 78- 83.
28	张可鑫, 席志红. 基于最大相关熵卡尔曼滤波的UWB室内定位算法[J]. 应用科技, 2024, 51 (3): 98- 104.
	ZHANG K X, XI Z H. Ultra wide band indoor location algorithm based on maximum correlation entropy Kalman filter[J]. Applied Science and Technology, 2024, 51 (3): 98- 104.
29	李琼, 考月英, 张莹, 等. 面向无人机航拍图像的目标检测研究综述[J]. 图学学报, 2024, 45 (6): 1145- 1164.
	LI Q, KAO Y Y, ZHANG Y, et al. Review on object detection in UAV aerial images[J]. Journal of Graphics, 2024, 45 (6): 1145- 1164.
30	YE T, ZHAO Z Y, ZHANG J, et al. Low-altitude small-sized object detection using lightweight feature-enhanced convolutional neural network[J]. Journal of Systems Engineering and Electronics, 2021, 32 (4): 841- 853. doi: 10.23919/JSEE.2021.000073
31	YAN J H, WANG Z G, WANG S F. Real-time tracking of deformable objects based on MOK algorithm[J]. Journal of Systems Engineering and Electronics, 2016, 27 (2): 477- 483. doi: 10.1109/JSEE.2016.00050
32	WEI Z Q, JI X P, WANG P. Real-time moving object detection for video monitoring systems[J]. Journal of Systems Engineering and Electronics, 2006, 17 (4): 731- 736. doi: 10.1016/S1004-4132(07)60007-3
33	HUANG X Z, TIAN Y P. Asynchronous distributed localization in networks with communication delays and packet losses[J]. Automatica, 2018, 96, 134- 140. doi: 10.1016/j.automatica.2018.06.048

无人机类型	传感器	参数	数值
所有无人机	航姿系统	俯仰角误差/（°）	0.2（RMS）
		滚转角误差/（°）	0.2（RMS）
		偏航角误差/（°）	1（RMS）
	加速度计	零偏误差/g	1.6×10⁻⁵
	加速度计	一阶马尔可夫相关时间/s	1800
	数据链	测距噪声/m 通信方式	30 轮询通信
	相机	测角误差/（°）视场宽度/（°）	1 120
锚节点	GNSS	位置误差/m	10
锚节点	GNSS	速度误差/（m/s）	0.2

情况	经度误差	纬度误差	高度误差
无修正	259.98	170.87	4.54
有修正	12.88	8.27	2.96

滤波方式	经度误差	纬度误差	高度误差
EKF	17.61	14.47	5.79
MCC-KF	12.88	8.27	2.96

[1]	张岳, 王晶, 王鼎衡, 代昌华, 段援朝, 刘保荣, 张霄嵩. 基于行人轨迹预测的无人车动态避障方法[J]. 系统工程与电子技术, 2025, 47(7): 2371-2382.
[2]	卢山, 张世源, 侯月阳, 张晓彤, 李晴. 深空探测中考虑乘性噪声影响的自主导航滤波算法设计[J]. 系统工程与电子技术, 2025, 47(1): 287-295.
[3]	李卫斌, 秦晨浩, 张天一, 毛鑫, 杨东浩, 纪文搏, 侯彪, 焦李成. 综述: 类脑智能导航建模技术及其应用[J]. 系统工程与电子技术, 2024, 46(11): 3844-3861.
[4]	赵太飞, 程敏花, 吕鑫喆, 郑博睿. 无人机蜂群中紫外光隐秘通信能耗均衡路由算法[J]. 系统工程与电子技术, 2021, 43(1): 251-257.
[5]	张阳, 司光亚, 王艳正. 无人机蜂群电磁作战概念仿真[J]. 系统工程与电子技术, 2020, 42(7): 1510-1518.
[6]	连捷, 姚晓先, 郭致远. 旋转弹舵机控制滞后及延迟补偿时间分析[J]. 系统工程与电子技术, 2018, 40(6): 1318-1324.
[7]	李璟璟, 张玉兔, 王文从, 胡慧君, 邵思霈. X射线源/地心矢量观测的卫星自主导航应用[J]. 系统工程与电子技术, 2018, 40(11): 2534-.
[8]	徐国栋，李鹏飞，董立珉，侯天蕊. 卫星自主轨道估计方法及其高阶非线性滤波器设计[J]. 系统工程与电子技术, 2014, 36(1): 117-122.
[9]	王鹏, 张迎春. 基于磁强计/太阳敏感器的自主导航方法[J]. Journal of Systems Engineering and Electronics, 2013, 35(1): 132-137.
[10]	杨文博, 李少远. 基于强跟踪UKF的航天器自主导航间接量测滤波算法[J]. Journal of Systems Engineering and Electronics, 2011, 33(11): 2485-2491.
[11]	刘贞, 王祁, 丁明理. 基于模糊控制的WSN移动节点自主导航算法[J]. Journal of Systems Engineering and Electronics, 2009, 31(1): 137-141.
[12]	刘贞, 王祁, 丁明理. 基于模糊控制的WSN移动节点自主导航算法[J]. Journal of Systems Engineering and Electronics, 2009, 31(01): 137-141.