电子学报 ›› 2019, Vol. 47 ›› Issue (11): 2392-2398.DOI: 10.3969/j.issn.0372-2112.2019.11.022

• 学术论文 • 上一篇    下一篇

基于部分平行ADMM求解谱寻求问题的时频分析

胡园园, 罗倩, 段中钰, 王嘉浩   

  1. 北京信息科技大学信息与通信工程学院, 北京 100020
  • 收稿日期:2018-12-17 修回日期:2019-05-13 出版日期:2019-11-25 发布日期:2019-04-28
  • 通讯作者: 胡园园
  • 作者简介:罗倩 女,1965年生于山西太原,现为北京信息科技大学副教授,硕士生导师.主要研究方向为信号与信息处理,大数据处理.E-mail:luoqian@bistu.edu.com;段中钰 男,1977年生于黑龙江鹤岗,现为北京信息科技大学副教授,硕士生导师.主要研究方向为信号与信息处理,数据挖掘.E-mail:duanzhongyu@bistu.edu.cn
  • 基金资助:
    北京市教委科研计划项目(No.KM201811232009)

Time-Frequency Analysis Based on Partly Parallel ADMM Solving Spectral Pursuit Problem

HU Yuan-yuan, LUO Qian, DUAN Zhong-yu, WANG Jia-hao   

  1. School of Information and Communication Engineering, Beijing Information Science&Technology University, Beijing 100020, China
  • Received:2018-12-17 Revised:2019-05-13 Online:2019-11-25 Published:2019-04-28

摘要: 经典的非参数谱分析方法使用滑动窗口来捕捉大多数时间序列的频谱特性,然而这种方法不能很好地应用在时间序列的时频谱是时间连续的信号上.对于一些其时频谱满足时间连续频率稀疏的非平稳信号,提出了一种利用部分平行交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)求解谱寻求问题用于此类信号的时频分析方法.对一段加噪声的仿真信号和一段EEG(脑电)信号使用提出的方法进行时频分析.仿真结果表明:与短时傅里叶的分析方法相比,提出的方法不仅提高了时频谱的频率分辨率和时间分辨率,还有效抑制了噪声.最后从ADMM算法停止准则的角度说明了算法的收敛.

关键词: 非参数谱分析, 滑动窗口, 部分平行ADMM, 谱寻求, 时频分析, 频率分辨率, 时间分辨率

Abstract: Classical nonparametric spectral analysis uses sliding windows to capture the dynamic nature of most real-world time series,however,this universally accepted approach fails to exploit the temporal continuity in the data.For some non-stationary signals that are smooth in time and sparse in frequency,a method of computing the spectrum pursuit estimate by using partly parallel alternating direction method of multipliers (ADMM) was used to obtain time-frequency analysis of this signal.A time-frequency analysis was performed on simulated and real human EEG data using the proposed method.The simulation results show that the proposed method not only effectively improves time and frequency resolution of the spectrum,but also effectively suppresses the noise than short-time Fourier transform.Finally,the convergence of the algorithm was illustrated from the perspective of stopping criterion of ADMM algorithm.

Key words: nonparametric spectral analysis, sliding windows, partly parallel Alternating Direction Method of Multipliers (ADMM), spectrum pursuit, time-frequency analysis, frequency resolution, time resolution

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