Abstract：The clustering analysis of space targets is an e ective method for supporting command and control, such as target analysis, attribute classification and identity recognition. However, the feature of the characteristics data is hard to identify, which results in the ineciency of data clustering. Firstly, the convolutional neural network is used to extract features for obtaining high-quality data. Secondly, the K-means algorithm is used for clustering the feature data. Experimental results show that the method improves the accuracy and performance of clustering analysis significantly, which can enhance the ability of analyzing space targets, and support the decisions making on the space situation command.
王文竹, 李智, 来嘉哲, 方宇强. 基于卷积神经网络的空间目标特性聚类分析研究[J]. 指挥与控制学报, 2020, 6(2): 141-146.
WANG Wen-Zhu, LI Zhi, LAI Jia-Zhe, FANG Yu-Qiang. Clustering Analysis of Space Object Characteristics Based on Convolutional Neural Network. Journal of Command and Control, 2020, 6(2): 141-146.