基于空间多特征综合推理的航迹航路关联

doi:10.7527/S1000-6893.2015.0318

Abstract

Abstract:

Considering track classification problem, the application of complex reasoning in the multi-feature track decision is studied. Firstly, according to the requirements of air traffic control system for airway and flight, an association model of track-to-airline is developed. Secondly, similarities between target features(position, direction) and information of known tracks are computed, basic belief assignments are constructed and then the target single feature classification results are obtained by fusion. The introduction of meta-class brings out the generalized credit classification for targets class. A multi-feature discount method is developed, giving the discount on features' basic belief assignments before fusion to get the target multi-feature classification results. The simulations and the test on real data of air traffic control system show that the method not only make the track classification, but also decrease the fault rate of classification.

Key words: track classification, classification algorithms, space multi-feature, decision models, generalized credit classification

CLC Number:

V219
TN958

LIANG Yan, WANG Xiaohua, LI Li, ZHANG Jinfeng, SHI Zhiyuan, YANG Feng. Track-to-airline association based on multi-feature reasoning[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016, 37(5): 1595-1602.

References

[1] 王增福, 潘泉, 陈丽平,等. 基于航路-航迹关联的天波超视距雷达航迹分类[J]. 系统工程与电子技术, 2012, 34(10):2018-2022 WANG Z F, PAN Q, CHEN L P, et al. Tracks classification based on airway-track association for over-the-horizon radar[J]. Systems Engineering and Electronics, 2012, 34(10):2018-2022(in Chinese).
[2] HAYKIN S, STEHWIEN W, DENG C, et al. Classification of radar clutter in an air traffic control environment[J]. Proceedings of the IEEE, 1991, 79(6):742-772.
[3] NOYES S P. Track classification in naval defense radar using fuzzy logic[C]//Proceedings of Colloquium on Target Tracking and Data Fusion. Pisscataway, NJ:IEEE Press, 1998:1-6.
[4] GHADAKI H. Feature formation and classification of radar targets[D]. Toronto:University of Toronto, 2006:14-15.
[5] 程铭, 陈宇寒. 飞行计划和雷达航迹关联目标识别算法[J]. 四川兵工学报, 2010, 31(5), 107-109. CHENG M, CHEN Y H. Target recognition algorithm of association with flight plan and radar dynamic track[J]. Journal of Sichuan Ordnance, 2010, 31(5):107-109(in Chinese).
[6] OXENHAM M. Automatic air target to airline association[C]//Proceedings of the 3rd International Conference on Information. Piscataway, NJ:IEEE Press, 2000:4-19.
[7] MAFTIN H. Multi-sensor track classification in ro-torcraft pilot's associate data fusion[C]//Proceedings of 53th Annual Forum-American Helicopter Society. Virginia:American Helicopter Society, 1997:271-280.
[8] LEUNG H, WU J. Bayesian and dempster-shafer target identification for radar surveillace[J]. Aerospace and Electronic Systems, 2000, 36(2):432-447.
[9] MERCIER D, LEFÈVRE É, JOLLY D. Object association with belief functions, an application with vehicles[J]. Information Sciences, 2011, 181(24):5485-5500
[10] WARTELLE N, DELMOTTE F, GAQUER D. Classification de scènes à nombre d'objets inconnu[C]//MAnifestation des Jeunes Chercheurs STIC, Lorient:Presse Universitaires Française (PUF), 2006:22-24.
[11] ZOGHBY N, CHERFAOUI V. Optimal object association from pairwise evidential mass functions[C]//Proceedings of the 16th International Conference on Information Fusion. Pisscataway, NJ:IEEE Press, 2013:774-780.
[12] HACHOUR S, DELMOTTE F, MERCIER D. Tracking and identification of multiple targets[C]//Proceedings of the 7th Workshop Interdisciplinaire sur la Sécurité Globale. Troyes, France:Agence Nationale de Recherche (ANR), 2013:13-18.
[13] HACHOUR S, DELMOTTE F, MERIER D, et al. Object tracking and credal classification with kinematic data in a multi-target context[J]. Information Fusion, 2014, 20:174-188.
[14] DENOEUX T, MASSON M H. EVCLUS:Evidential clus-tering of proximity data[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics, 2004, 34(1):95-109.
[15] MASSON M H, DENOEUX T. ECM:An evidential version of the fuzzy c-means algorithm[J]. Pattern Recognition. 2008, 41(4):1384-1397.
[16] BEZDEK J C. Pattern recognition with fuzzy objective function algorithms[M]. New York:Plenum Press, 1981:203-239.
[17] DAVE R N. Clustering of relational data containing noise and outliers[C]//Proceedings of the 1998 IEEE International Conference on Fuzzy Systems.Piscataway, NJ:IEEE Press, 1998:1411-1416.
[18] ALTINCAY H.Ensembling evidential k-nearest neighbor classifiers through multi-modal perturbation[J]. Applied Soft Computing, 2007, 7(3):1072-1083.
[19] LIU Z, PAN Q, DEZERT J. Classification of uncertain and imprecise data based on evidence theory[J]. Neuro Computing, 2014, 133:459-470.
[20] LIU Z, PAN Q, DEZERT J. A new belief-based K-nearest neighbor classification method[J]. Pattern Recognition, 2013, 46(3):834-844.

Track-to-airline association based on multi-feature reasoning

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Bin CHEN, Houyin XI, Xiaodong ZHANG, Min LUO, Zhidong GUO. Point cloud registration of space non-cooperative targets based on geodesic distance [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(1): 326336-326336.
[2]	. A fault-tolerant control scheme for UAV-UGV formation systems [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 0, (): 0-0.
[3]	WEN Qian, YANG Jiawei, WU Zeping, YANG Xixiang, ZHAO Hailong, WANG Zhixiang. An approximation modeling method of launch vehicles improved by fast cross-validation [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(9): 225967-225967.
[4]	LYU Shuaishuai, YANG Yu, WANG Binwen, YIN Chenfei. A crack identification method of aircraft structure based on improved FaceNet [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(6): 525463-525463.
[5]	WANG Yupeng, LYU Shuaishuai, YANG Yu, LI Jiaxin, WANG Yezi. Damage recognition of composite structures based on domain adaptive model [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(6): 526752-526752.
[6]	Xiang-Dan Zhao Wang zhisheng. Predictive fifth-degree cubature Kalman filter Method [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 0, (): 0-0.
[7]	. Dynamics Model Updating of Structures under High Temperature based on A Novel Particle Swarm Optimization Algorithm [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 0, (): 0-0.
[8]	GUO Lei, YUAN Yuan, QIAO Jianzhong, YU Xiang. Immune intelligence of unmanned system [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(11): 24618-024618.
[9]	JIANG Jun, ZHANG Bendong, WANG Kai, LI Wenyuan, ZHANG Chaohai. Partial discharge rule of more-electric-aircraft with pulse voltage waveform [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(9): 323889-323889.
[10]	XI Zhifei, XU An, KOU Yingxin, LI Zhanwu, YANG Aiwu. Target threat assessment in air combat based on PCA-MPSO-ELM algorithm [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(9): 323895-323895.
[11]	WANG Li, SUN Xiuqing, ZHANG Chunming, LI Xiao, WU Fenzhi. Fast online estimation method for installing matrix between all-time star tracker and inertial navigation system [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(8): 624117-624117.
[12]	LIANG Fei, XIAO Yingchun, LU Nan, ZHU Feng. Characteristics analysis of electromagnetic emission on VOR caused by neutral section arcs [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(8): 323705-323705.
[13]	LIN Jiaquan, SUN Fengshan, LI Yachong, ZHUANG Zibo. Prediction of aircraft cabin energy consumption based on IPSO-Elman neural network [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(7): 323614-323614.
[14]	HU Liping, LIANG Xiaolong, HE Lyulong, ZHANG Jiaqiong, REN Baoxiang, QI Duo. Construction method of aviation swarm decision rule base based on scenario analysis [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(S1): 723737-723737.
[15]	WANG Siyuan, WANG Gang, YUE Shaohua, SONG Yafei, LIU Jiayi. Threat assessment based on multilayer and multistage skyline selection method [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(5): 323494-323494.