北京航空航天大学学报 ›› 2017, Vol. 43 ›› Issue (9): 1773-1778.doi: 10.13700/j.bh.1001-5965.2016.0661

• 论文 • 上一篇    下一篇

基于随机森林的航天器电信号多分类识别方法

兰巍1, 贾素玲1, 宋世民2, 李可3   

  1. 1. 北京航空航天大学 经济管理学院, 北京 100083;
    2. 中国空间技术研究院, 北京 100194;
    3. 北京航空航天大学 航空科学与工程学院, 北京 100083
  • 收稿日期:2016-08-15 出版日期:2017-09-20 发布日期:2017-09-28
  • 通讯作者: 李可,E-mail:like@buaa.edu.cn E-mail:like@buaa.edu.cn
  • 作者简介:兰巍,女,博士研究生;主要研究方向:数据挖掘与机器学习;贾素玲,女,教授;主要研究方向:信息系统;宋世民,男,高级工程师;主要研究方向:卫星综合测试;李可,男,高级实验师;主要研究方向:环境控制与计算机测试
  • 基金资助:
    航空科学基金(2012XX1043);中央高校基本科研业务费专项资金(YWF-16-HKXY-017);国家自然科学基金(61773039)

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

LAN Wei1, JIA Suling1, SONG Shimin2, LI Ke3   

  1. 1. School of Economics and Management, Beijing University of Aeronautics and Astronautics, Beijing 100083, China;
    2. China Academy of Space Technology, Beijing 100194, China;
    3. School of Aeronautic Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
  • Received:2016-08-15 Online:2017-09-20 Published:2017-09-28
  • Supported by:
    Aeronautical Science Foundation of China (2012XX1043); the Fundamental Research Funds for the Central Universities (YWF-16-HKXY-017); National Natural Science Foundation of China (61773039)

摘要: 针对航天器电特性信号数据存在数据量大、特征维数高、计算复杂度大和识别率低等问题,提出基于主成分分析(PCA)的特征提取方法和随机森林(RF)算法,对原始数据进行降维,提高计算效率和识别率,实现对航天器电信号数据的快速、准确识别分类。随机森林算法在处理高维数据上具有优越的性能,但是考虑到时间复杂度问题,利用主成分分析方法对数据进行压缩和降维,在保证准确率的同时提高了计算效率。实验结果表明:与其他算法相比,针对航天器电特性信号数据,本文方法在准确率、计算效率和稳定性等方面均显示出优异的性能。

关键词: 航天器, 电信号识别, 主成分分析(PCA), 多分类, 随机森林(RF)

Abstract: 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.

Key words: spacecraft, electrical signal identification, principal component analysis (PCA), multi-classification, random forest (RF)

中图分类号: 


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