电子学报 ›› 2017, Vol. 45 ›› Issue (8): 2026-2030.DOI: 10.3969/j.issn.0372-2112.2017.08.031

• 学术论文 • 上一篇    下一篇

一种非平稳非线性频谱占用度时间序列分析方法

魏鸿浩, 贾云峰   

  1. 北京航空航天大学电子信息工程学院, 北京 100191
  • 收稿日期:2016-05-16 修回日期:2016-08-16 出版日期:2017-08-25
    • 通讯作者:
    • 魏鸿浩
    • 作者简介:
    • 贾云峰,男,1975年4月生于湖北省荆门市,2002年在国防科技大学获博士学位,2003-2005年于中科院电子所微波器件中心进行博士后研究,现为北京航空航天大学电子信息工程学院硕士生导师,北航电磁兼容技术研究所设计部部长,主要从事微波电磁场与电磁兼容预设计、评估和验证等研究工作.
    • 基金资助:
    • 国家自然科学基金 (No.61371007)

A Method for Analysis of Non-linear and Non-stationary Spectrum Occupancy Time Series

WEI Hong-hao, JIA Yun-feng   

  1. School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
  • Received:2016-05-16 Revised:2016-08-16 Online:2017-08-25 Published:2017-08-25
    • Supported by:
    • National Natural Science Foundation of China (No.61371007)

摘要: 针对传统频谱占用度分析模型由于未考虑序列的非线性非平稳特性,导致无法准确描述频谱占用度特性的问题,该文提出将集合经验模式分解(EEMD)方法与人工神经网络(ANN)的方法结合应用于频谱占用度时间序列建模方法中,采用EEMD+ANN的频谱占用度序列建模和预测方法.首先应用EEMD分解算法把原始频谱占用度时间序列分解成不同尺度的基本模态分量,再根据不同尺度的基本模态分量分别构建ANN模型,提高了模型针对复杂频谱占用度时间序列的学习能力.结合实测数据分析,表明该模型相对传统频谱占用度模型具有更高的拟合和预测精度,验证了该方法的正确性与有效性.

关键词: 电磁环境, 电磁频谱, 频谱占用度, 集合经验模式分解

Abstract: In order to analyze the non-linear and non-stationary spectrum occupancy time series which cannot be directly analyzed base on traditional time series method,a novel prediction modelling method of spectrum occupancy time series based on ensemble empirical mode decomposition (EEMD) and Artificial Neural Network (ANN) is proposed.Firstly,the spectrum occupancy time series is decomposed into serval intrinsic model function (IMF) so as to make every component stationary.Then in view of the stationary time series,a prediction ANN model is established correspondingly for each IMF.Simulative experiment for practical measured data shows that the proposed method has higher precision in comparison with other methods,i.e.,effective to non-linear and ono-stationary complicated spectrum occupancy time series prediction.

Key words: electromagnetic environment, electromagnetic spectrum, spectrum occupancy, ensemble empirical mode decomposition (EEMD)

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