基于动态估计反馈的灰色理论航迹关联算法

doi:10.12305/j.issn.1001-506X.2024.04.29

摘要/Abstract

摘要：

针对传统基于灰色理论航迹关联算法未充分利用航迹历史信息, 在密集目标以及时变噪声协方差等场景下关联正确率下降问题, 在传统灰色分析法基础上增加了一种动态估计反馈机制。改进后的算法引入了Sage-Husa估计器来实时估计传感器的噪声协方差作为评估输出数据质量的依据, 并使用Critic赋权法将实时噪声协方差转换为各时刻序贯权重, 从而保证最大程度上的利用航迹历史信息。仿真证明, 在密集目标与时变噪声协方差等特殊关联场景下, 所提算法明显优于传统灰色分析法以及模糊法、加权法等经典算法, 充分证明了所提算法的性能优越性以及鲁棒性。

关键词: 航迹关联, 多传感器, 数据融合, 灰色理论

Abstract:

In response to the problem that traditional grey theory based track association algorithms do not make full use of track history information and reduce association accuracy in scenarios such as dense targets and time-varying noise covariance, a dynamic estimation feedback mechanism based on traditional grey analysis methods is added. The improved algorithm introduces Sage-Husa estimator to estimate real-time sensor noise covariance as a basis for evaluating output data quality, and uses Critical weighting method to convert real-time noise covariance into sequential weights at each time, ensuring maximum utilization of track history information. Simulation has shown that in special correlation scenarios such as dense targets and time-varying noise covariance, the proposed algorithm is significantly superior to traditional grey analysis methods, as well as classical algorithms such as fuzzy and weighted methods, fully demonstrating the performance superiority and robustness of the proposed algorithm.

Key words: track association, multiple sensors, data fusion, grey theory

中图分类号:

邱建杰, 蔡益朝, 李浩, 黄权印. 基于动态估计反馈的灰色理论航迹关联算法[J]. 系统工程与电子技术, 2024, 46(4): 1401-1411.

Jianjie QIU, Yichao CAI, Hao LI, Quanyin HUANG. Grey theory track association algorithm based on dynamic estimation feedback[J]. Systems Engineering and Electronics, 2024, 46(4): 1401-1411.

图/表 10

图1

表1

图2

图3

表2

表3

图4

图5

图6

图7

参考文献 31

1	韩崇昭, 朱洪艳, 段战胜.多源信息融合[M].2版北京: 清华大学出版社,2010.
	HAN C Z , ZHU H Y , DUAN Z S .Multi-source information fusion[M].2nd edBeijing: Press of Tsinghua University,2010.
2	NAZARI M , PASHAZADEH S , MOHAMMAD-KHANLI L .An adaptive density-based fuzzy clustering track association for distributed tracking system[J].IEEE Access,2019,7,135972-135981. doi: 10.1109/ACCESS.2019.2941184
3	SUN K, WEN Y J, CHEN X, et al. A method for track associa-tion using dynamic programming[C]//Proc. of the 6th International Conference on Control, Decision and Information Techno-logies, 2019: 1373-1378.
4	ZHANG X, HUANG H, CHEN Q L, et al. An improved track association and fusion method[C]//Proc. of the IEEE 14th International Conference on Signal Processing, 2018: 854-858.
5	MA M Y , WANG D J , ZHANG T , et al.Track-to-track association algorithm for passive multi-sensor system based on trajectory parameter[J].IET Radar, Sonar & Navigation,2021,15(4):348-358.
6	SINGER R A .Computer control of multiple site track data[J].Automation,1971,7(3):455-463.
7	BAR-SHALOM Y , FORTMAN T E .Tracking and association[M].New York: Aeademic Press,1988.
8	何友, 陆大, 彭应宁, 等.多传感器数据融合系统中两种新的航迹相关算法[J].电子学报,1997,25(9):10-14, 19.
	HE Y , LU D , PENG Y N , et al.Two new track correlation algorithms in a multisensor data fusion system[J].Acta Electronica Sinica,1997,25(9):10-14, 19.
9	AZIZ A M .Fuzzy track-to-track association and track fusion approach in distributed multisensor-multitarget multiple-attribute environment[J].Signal Processing,2007,87(6):1474-1492. doi: 10.1016/j.sigpro.2007.01.001
10	TUMMALA M, MIDWOOD S A. A fuzzy associative data fusion algorithm for vessel traffic system[R]. Monterey, Califomia: Naval Postgraduate School, 1998.
11	TUMMALA M, GLEM I, MIDWOOD S A. Multisensor data fusion for the vessel traffic system[R]. Monterey, Califomia: Naval Postgraduate School, 1996.
12	崔亚奇, 何友, 唐田田, 等.一种深度学习航迹关联方法[J].电子学报,2022,50(3):759-763.
	CUI Y Q , HE Y , TANG T T , et al.A deep learning track correlation method[J].Acta Electronica Sinica,2022,50(3):759-763.
13	黄虹玮, 刘玉娇, 沈卓恺, 等.基于深度学习网络模型的端到端航迹关联[J].计算机科学,2020,47(3):200-205.
	HUANG H W , LIU Y J , SHEN Z K , et al.End-to-end track association based on deep learning network model[J].Computer Science,2020,47(3):200-205.
14	赵翻东, 蔡益朝, 李浩.基于GRNN神经网络的多目标航迹关联[J].信息系统工程,2021,(7):135-136, 140.
	ZHAO F D , CAI Y C , LI H .Multitarget track association based on GRNN neural network[J].Information System Engineering,2021,(7):135-136, 140.
15	邓聚龙.灰色理论基础[M].武汉: 华中科技大学出版社,2003:30-35.
	DENG J L .Fundamentals of grey theory[M].Wuhan: Huazhong University of Science & Techonlogy Press,2003:30-35.
16	刘思峰, 党耀国, 方志耕.灰色系统理论及其应用[M].北京: 科学出版社,2004:50-75.
	LIU S F , DANG Y G , FANG Z G .Grey system theory and applications[M].Beijing: Science Press,2004:50-75.
17	GUAN X , HE Y , YI X .Grey track-to-track correlation algorithm for distributed multitarget tracking system[J].Signal Processing,2006,86(11):3448-3455. doi: 10.1016/j.sigpro.2006.03.001
18	衣晓, 关欣, 何友.分布式多目标跟踪系统的灰色航迹关联模型[J].信号处理,2005,21(6):653-655.
	YI X , GUAN X , HE Y .Modified gray track correlatioon model for distributec multitarget tracking system[J].Signal Processing,2005,21(6):653-655.
19	熊伟, 张晶炜, 何友.基于多维分配和灰色理论的航迹关联算法[J].电子与信息学报,2010,32(4):898-901.
	XIONG W , ZHANG J W , HE Y .Track correlation algorithm based on multi-dimension assignment and gray theory[J].Journal of Electronics & Information Technology,2010,32(4):898-901.
20	董凯, 关欣, 王海鹏, 等.基于序贯修正灰关联度的全局最优航迹关联算法[J].电子与信息学报,2014,36(8):1939-1945.
	DONG K , GUAN X , WANG H P , et al.Global optimal track association algorithm based on sequential modified grey association degree[J].Journal of Electronics & Information Technology,2014,36(8):1939-1945.
21	靳冰洋, 刘峥, 秦基凯.基于灰色关联度的两级实时航迹关联算法[J].兵工学报,2020,41(7):1330-1338.
	JI B Y , LIU Z , QIN J K .Two-stage real-time track correlation algorithm based on gray correlation[J].Acta Armamentarii,2020,41(7):1330-1338.
22	LUO Z Y , FU Z H , XU Q W .An adaptive multi-dimensional vehicle driving state observer based on modified Sage-Husa UKF algorithm[J].Sensors,2020,20(23):6889. doi: 10.3390/s20236889
23	ZHENG B Q , FU P C , LI B Q , et al.A robust adaptive unscented Kalman filter for nonlinear estimation with uncertain noise covariance[J].Sensors,2018,18(3):808. doi: 10.3390/s18030808
24	叶泽浩, 宋亚伟, 陈传生, 等.基于噪声估计的自适应无迹卡尔曼滤波算法[J].空天预警研究学报,2022,36(4):242-246, 251.
	YE Z H , SONG Y W , CHEN C S , et al.Adaptive unscented Kalman filtering algorithm based on noise estimation[J].Journal of Air & Space Early Warning Research,2022,36(4):242-246, 251.
25	张瑜, 吴凯, 郭杰, 等.基于数据质量评估的自适应序贯航迹关联算法[J].系统工程与电子技术,2022,44(11):3477-3485.
	ZHANG Y , WU K , GUO J , et al.An adaptive sequential track-association algorithm based on data quality[J].Systems Engineering and Electronics,2022,44(11):3477-3485.
26	DIAKOULAKI D , MAVROTAS G , PAPAYANNAKIS L .Determining objective weights in multiple criteria problems, the critic method[J].Computer & Operations Research,1995,22(7):763-770.
27	胡涛, 王栋, 孙曜, 等.基于改进CRITIC-LRA和灰色逼近理想解排序法的空战威胁评估[J].兵工学报,2020,41(12):2561-2569.
	HU T , WANG D , SUN Y , et al.Air combat threat assessment of improved CRITIC-LRA and grey TOPSIS[J].Acta Armamentarii,2020,41(12):2561-2569.
28	王坚浩, 王龙, 张亮, 等.灰色群组聚类和改进CRITIC赋权的供应商选择VIKOR多属性决策[J].系统工程与电子技术,2023,45(1):155-164.
	WANG J H , WANG L , ZHANG L , et al.Supplier selection VIKOR multi-attribute decision making of group clustering and improved CRITCI weighting[J].Systems Engineering and Electronics,2023,45(1):155-164.
29	李小元, 王中原, 常思江.在线弹道参数滤波与辨识方法分析[J].弹道学报,2020,32(2):29-34.
	LI X Y , WANG Z Y , CHANG S J .Online trajectory parameter filtering and identification method analysis[J].Journal of Ballistics,2020,32(2):29-34.
30	SAGE A, HUSA G. Algorithms for sequential adaptive estimation of prior statistics[C]//Proc. of the IEEE 8th Symposium on Adaptive Processes Decision and Control, 1969.
31	WAN E A, MERWE R. The unscented Kalman filter for nonlinear estimation[C]//Proc. of the IEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium, 2000.

场景	X方向速度分布/(m/s)	Y方向速度分布/(m/s)	Z方向速度分布/(m/s)	X方向加速度分布/(m/s²)	Y方向加速度分布/(m/s²)	Z方向加速度分布/(m/s²)	ρ
场景1	N(100, 1)	N(100, 1)	N(50, 1)	U(-10, 10)	U(-10, 10)	U(-1, 1)	0.4
场景2	N(10, 1)	N(10, 1)	N(5, 1)	U(-2, 2)	U(-2, 2)	U(-0.2, 0.2)	0.4
场景3	10	5	1	U(-0.1, 0.1)	U(-0.1, 0.1)	0.01	0.4

场景	σ_x²	σ_y²	σ_z²	ε	ε_min	ε_max	目标数量/批
场景1	20	20	10	0.9	0.6	0.9	50
场景2	N(60, 2)	N(60, 2)	N(30, 2)	0.9	0.6	0.9	50
场景3	N(60, 2)	N(60, 2)	N(30, 2)	0.9	0.6	0.9	50

场景	算法
场景	加权法	模糊法	灰色分析法	本文算法
场景1	72.05	94.06	96.54	98.32
场景2	36.04	54.92	81.06	97.16
场景3	31.26	50.24	75.92	93.65

场景	算法
场景	加权法	模糊法	灰色分析法	本文算法
场景1	3.028 3	4.328 1	5.463 4	5.552 8
场景2	5.176 3	7.798 0	9.722 5	10.049 1
场景3	5.610 8	8.978 1	10.654 2	11.298 2

[1]	韦道知, 张曌宇, 谢家豪, 李宁. 基于改进Actor-Critic算法的多传感器交叉提示技术[J]. 系统工程与电子技术, 2023, 45(6): 1624-1632.
[2]	国佳恩, 周正, 曾睿. 多局部节点异步航迹快速关联算法[J]. 系统工程与电子技术, 2023, 45(3): 669-677.
[3]	韩伟, 王国师, 晏凯, 宋亚伟. 基于多维分配的多普勒盲区目标点迹-航迹关联方法[J]. 系统工程与电子技术, 2023, 45(10): 3091-3097.
[4]	刘康, 何明浩, 韩俊, 冯明月, 都兴霖. 基于多传感器的雷达对抗侦察数据融合算法[J]. 系统工程与电子技术, 2023, 45(1): 101-107.
[5]	胡泰洋, 张晋宇, 卢海梁, 李鹏飞, 李一楠, 吕容川. 基于数据融合的分布式综合孔径微波辐射高分辨率成像算法[J]. 系统工程与电子技术, 2022, 44(8): 2403-2409.
[6]	关欣, 国佳恩. 基于迭代离散度序贯检测的航迹抗差关联算法[J]. 系统工程与电子技术, 2022, 44(8): 2498-2505.
[7]	刘康, 何明浩, 韩俊, 王庚. 基于本征向量和Jousselme距离的高冲突证据融合方法[J]. 系统工程与电子技术, 2022, 44(7): 2175-2180.
[8]	衣晓, 曾睿, 曹昕莹. 利用k近邻区间距离的异步抗差航迹关联算法[J]. 系统工程与电子技术, 2022, 44(5): 1475-1482.
[9]	谢家豪, 黄树彩, 韦道知, 张曌宇, 王文豪. 基于P_EV准则的不确定混合多传感器联盟求解[J]. 系统工程与电子技术, 2022, 44(3): 819-826.
[10]	董晨, 帅逸仙, 周金鹏, 赖鹏, 程仙垒. 网络化多传感器-多武器协同防空任务规划[J]. 系统工程与电子技术, 2022, 44(12): 3738-3746.
[11]	张瑜, 吴凯, 郭杰, 葛志闪, 张宝超. 基于数据质量评估的自适应序贯航迹关联算法[J]. 系统工程与电子技术, 2022, 44(11): 3477-3485.
[12]	衣晓, 曾睿. 基于k近邻平均距离的异步航迹关联算法[J]. 系统工程与电子技术, 2022, 44(11): 3515-3521.
[13]	张路路, 陈思雅, 金光. 航天器抗辐射等效试验评估建模[J]. 系统工程与电子技术, 2021, 43(9): 2673-2677.
[14]	时晨光, 董璟, 周建江, 汪飞. 飞行器射频隐身技术研究综述[J]. 系统工程与电子技术, 2021, 43(6): 1452-1467.
[15]	张佳琦, 陶海红, 张修社. 量测点迹空间聚类的多传感器多帧检测算法[J]. 系统工程与电子技术, 2021, 43(6): 1533-1540.