Multi-classification spacecraft electrical signal identification method based on random forest

被引：0

作者：

Lan W. ^{[1
]}

Jia S. ^{[1
]}

Song S. ^{[2
]}

Li K. ^{[3
]}

机构：

[1] School of Economics and Management, Beijing University of Aeronautics and Astronautics, Beijing

[2] China Academy of Space Technology, Beijing

[3] School of Aeronautic Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing

来源：

Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2017年 / 43卷 / 09期

关键词：

Electrical signal identification; Multi-classification; Principal component analysis (PCA); Random forest (RF); Spacecraft;

D O I：

10.13700/j.bh.1001-5965.2016.0661

中图分类号：

学科分类号：

摘要：

The spacecraft electrical signal characteristic data have problems such as large amount, high-dimensional features, high computational complexity and low identification rate. The feature extraction method of principal component analysis (PCA) and random forest (RF) algorithm was proposed to reduce the dimensionality of the original data, improve the computational efficiency and identification rate, and achieve rapid and accurate identification of spacecraft electrical signal data. The random forest algorithm has superior performance in dealing with high-dimensional data. However, considering the time complexity, the method of PCA was used to compress the data and reduce the dimension in order to ensure the accuracy of the classification and improve the computational efficiency. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in accuracy, computational efficiency, and stability when dealing with spacecraft electrical signal data. © 2017, Editorial Board of JBUAA. All right reserved.

引用

页码：1773 / 1778

页数：5

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