Computer Science ›› 2022, Vol. 49 ›› Issue (9): 70-75.doi: 10.11896/jsjkx.210800203

• Database & Big Data & Data Science • Previous Articles     Next Articles

Aerial Target Grouping Method Based on Feature Similarity Clustering

CHAI Hui-min1,2, ZHANG Yong2, FANG Min1   

  1. 1 School of Computer Science and Technology,Xidian University,Xi'an 710071,China
    2 Science and Technology on Electro-Optical Information Security Control Laboratory,Tianjin 300308,China
  • Received:2021-08-23 Revised:2022-03-11 Online:2022-09-15 Published:2022-09-09
  • About author:CHAI Hui-min,born in 1976,Ph.D,associate professor.Her main research interests include information fusion and situation awareness.
  • Supported by:
    Defense Pre-Research Foundation of China(6142107190106).

Abstract: In order to solve the problems that the number of clusters needs to be given and the sensitivity to the initial positions of the cluster centers while clustering algorithm is utilized for target grouping,a novel aerial target grouping method based on feature similarity clustering is proposed.Firstly,the similarity between targets is calculated and the similarity matrix is constructed.Then,the connected branches of the similarity matrix are calculated to obtained the group center structure and the isolated target points are detected.The number of group center structures is the number of clusters.Finally,the targets which are not belonging to the group center structure and the isolated points are clustered into the closest group center structure.It makes the clustering process no longer depend too much on the initialization of the cluster centers.Experimental results show that the proposed methodcan correctly identify the group center structure and detect the isolated points.Furthermore,its the clustering accuracy is higherthan that of other four clustering algorithms.

Key words: Aerial target grouping, Clustering algorithm, Target similarity, Group center structure

CLC Number: 

  • TP391.9
[1]ZHAO Z G,LI J L.Development Evolution and Technological Trend of Information Fusion[J].Command Information System and Technology,2017,8(1):1-8.
[2]ZHAO Y Y.Target Clustering and Recognition in Battlefield Situation Assessment[D].Xi'an:Xidian University,2019.
[3]YUAN D P,ZHENG J Y,SHI H S,et al.Target Grouping Algorithm Based on Multiple Combat Formations[J].Computer Science,2016,43(2):235-238,244.
[4]XING C.Research on the Method of Object Grouping in Flight Situation Awareness[D].Tianjin:Civil Aviation University of China,2017.
[5]RODRIGUEZ A,LAIO A.Machine learning Clustering by Fast Search and Find of Density Peaks[J].Science,2014,344(6191):1492-1496.
[6]DONG B.The Research of Aircraft Object Clustering Application Based on Cluster Analysis[D].Xi'an:Xidian University,2016.
[7]LI H L.Research of Situation Data Management and TargetClustering Technology Based on Big Data[D].Chengdu:University of Electronic Science and Technology of China,2019.
[8]LEI M,TAN A H,WUNSCH D C.Adaptive Scaling of Cluster Boundaries for Large-Scale Social Media Data Clustering[J].IEEE Transaction on Neural Networks and Learning Systems,2016,27(12):2656-2669.
[9]YE X L,ZHAO J Y,CHEN Y,et al.Bayesian Adversarial Spectral Clustering with Unknown Cluster Number[J].IEEE Transactions on image processing,2020,29:8506-8518.
[10]TAO Y,JIANG X P.Intelligent Target Clustering Algorithm Based on Deep Auto-encoder Network[J].Command Control and Simulation,2020,42(6):52-58.
[11]ZHANG Y L,LIU N N,WANG Z W.Target Grouping and Target Motion State Prediction Based on Neural Network[J].Shipboard Electronic Countermeasure,2020,43(3):7-12.
[12]FAN Z H,SHI B H,CHEN J Y,et al.Improved spaces partition based target clustering algorithm[J].Systems Engineering and Electronics,2017,39(5):991-995.
[13]ZHANG D N,AI W.Design and Realization of Target Grouping in Situation Assessment[J].Radio Engineering,2016,46(11):42-46.
[14]TAO Y,JIANG X P.Radar Detection Target Clustering Algorithm Based on Similarity Matrix[J].Fire Control Radar Technology,2018,47(1):40-44.
[15]SNIDARO L,VISENTINI I,BRYAN K.Fusing uncertainknowledge and evidence for martime situational awareness via Markov Logic Networks[J].Information Fusion,2015,21(1):159-172.
[16]TENENBAUM J B,SILVA V,LANGFORD J C.A Global Geometric Framework for Nonlinear Dimensionality Reduction[J].Science,2000,290(5500):2319-2323.
[17]ARTHUR D.k-means++:the advantages of careful seeding[C]//Proceedings of the Eighteenth Annual ACM-SIAM SYMPosium on Discrete Algorithms,2007.Society for Industrial and Applied Mathematics,2007:1027-1035.
[18]BEZDEK J C.Pattern Recognition with Fuzzy Objective Function Algorithms[M]//Advanced Applications in Pattern Recognition.Berlin:Springer,1981:65-86.
[19]KATSAVOUNIDIS I.A new initialization technique for genera-lized lloyd iteration[J].IEEE Signal Processing Letters,1994,1(10):144-146.
[1] ZHANG Ya-di, SUN Yue, LIU Feng, ZHU Er-zhou. Study on Density Parameter and Center-Replacement Combined K-means and New Clustering Validity Index [J]. Computer Science, 2022, 49(1): 121-132.
[2] LI Shan, XU Xin-zheng. Parallel Pruning from Two Aspects for VGG16 Optimization [J]. Computer Science, 2021, 48(6): 227-233.
[3] TANG Xin-yao, ZHANG Zheng-jun, CHU Jie, YAN Tao. Density Peaks Clustering Algorithm Based on Natural Nearest Neighbor [J]. Computer Science, 2021, 48(3): 151-157.
[4] WANG Mao-guang, YANG Hang. Risk Control Model and Algorithm Based on AP-Entropy Selection Ensemble [J]. Computer Science, 2021, 48(11A): 71-76.
[5] WANG Wei-dong, XU Jin-hui, ZHANG Zhi-feng, YANG Xi-bei. Gaussian Mixture Models Algorithm Based on Density Peaks Clustering [J]. Computer Science, 2021, 48(10): 191-196.
[6] ZHANG Yu, LU Yi-hong, HUANG De-cai. Weighted Hesitant Fuzzy Clustering Based on Density Peaks [J]. Computer Science, 2021, 48(1): 145-151.
[7] XU Shou-kun, NI Chu-han, JI Chen-chen, LI Ning. Image Caption of Safety Helmets Wearing in Construction Scene Based on YOLOv3 [J]. Computer Science, 2020, 47(8): 233-240.
[8] DENG Ding-sheng. Application of Improved DBSCAN Algorithm on Spark Platform [J]. Computer Science, 2020, 47(11A): 425-429.
[9] ZHANG Jian-xin, LIU Hong, LI Yan. Efficient Grouping Method for Crowd Evacuation [J]. Computer Science, 2019, 46(6): 231-238.
[10] HU Chuang, YANG Geng, BAI Yun-lu. Clustering Algorithm in Differential Privacy Preserving [J]. Computer Science, 2019, 46(2): 120-126.
[11] ZHANG Tian-zhu, ZOU Cheng-ming. Study on Image Classification of Capsule Network Using Fuzzy Clustering [J]. Computer Science, 2019, 46(12): 279-285.
[12] CHEN Zi-hao, LI Qiang. Improved PBFT Consensus Mechanism Based on K-medoids [J]. Computer Science, 2019, 46(12): 101-107.
[13] CHEN Chun-tao, CHEN You-guang. Influence Space Based Robust Fast Search and Density Peak Clustering Algorithm [J]. Computer Science, 2019, 46(11): 216-221.
[14] CHEN Jing-jie, CHE Jie. IK-medoids Based Aircraft Fuel Consumption Clustering Algorithm [J]. Computer Science, 2018, 45(8): 306-309.
[15] WANG Hui-yan,ZHANG Teng-fei,MA Fu-min. Rough K-means Algorithm with Self-adaptive Weights Measurement Based on Space Distance [J]. Computer Science, 2018, 45(7): 190-196.
Full text



No Suggested Reading articles found!