An unsupervised classification method of flight states for hypersonic targets based on hyperspectral features
An unsupervised classification method of flight states for hypersonic targets based on hyperspectral features
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DOI：
10.1016/j.cja.2022.11.028
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发表时间：
2022-12
影响因子：
5.7
通讯作者：
S. Yuan;Lei Shi;Yutong Zhai;Bo Yao;Fangyan Li;Yuefan Du
中科院分区：
文献类型：
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作者：
S. Yuan;Lei Shi;Yutong Zhai;Bo Yao;Fangyan Li;Yuefan Du
针对近年来高超声速目标快速发展给航天防御带来的挑战，基于高速飞行过程中被等离子体鞘覆盖的高超声速目标的高光谱特征，开展了高超声速目标飞行状态的非监督分类研究。首先，定义了超级节点的新概念，通过降低HHS的类内变异性来提高分类精度。然后，根据相同飞行状态下高频信号波动特征相似的先验理论知识，利用高频信号曲线的频域信息减少特征冗余。最后，设计了一种基于密度峰值聚类(DPC)的无监督飞行状态分类方法，在消除类内变异和特征维冗余影响的基础上对飞行状态进行分类。在高超声速飞行器典型飞行状态的模拟高光谱数据集和实际观测高光谱数据集上与传统的分类算法进行了比较。结果表明，该方法在整体准确率、平均准确率和Kappa系数方面具有竞争优势。
In response to the challenges of aerospace defense caused by the rapid development of hypersonic targets in recent years, the research on the unsupervised classification of flight states for hypersonic targets is carried out in this paper, which is based on the Hyperspectral Features (HFs) of hypersonic targets covered with plasma sheath during high-speed flight. First, a new concept of the super node is defined to improve classification accuracy by alleviating the intraclass variability of HFs. Then, the frequency domain information of the curve of HFs is utilized to reduce the feature redundancy according to the prior theoretical knowledge that the fluctuation characteristics of HFs of the same flight states are similar. Finally, an unsupervised classification method based on the Density Peak Clustering (DPC) for HFs is designed to class flight states after eliminating the impact of intraclass variability and feature dimension redundancy. The proposal is compared with the traditional classification algorithms on simulated hyperspectral data sets of typical flight states of the hypersonic vehicle and an actual-observation hyperspectral data set. The results indicate that the performance of our proposal has competitive advantages in terms of Overall Accuracy (OA), Average Accuracy (AA) and Kappa coefficient.