计算机应用 ›› 2012, Vol. 32 ›› Issue (01): 272-278.DOI: 10.3724/SP.J.1087.2012.00272

• 典型应用 • 上一篇    下一篇

非平稳信号稀疏表示的研究发展

范虹,郭鹏,王芳梅   

  1. 陕西师范大学 计算机科学学院,西安 710062
  • 收稿日期:2011-07-12 修回日期:2011-09-25 发布日期:2012-02-06 出版日期:2012-01-01
  • 通讯作者: 范虹
  • 作者简介:范虹(1969-),女,宁夏平罗人,副教授,博士,主要研究方向:信号处理、图像处理、模式识别;郭鹏(1987-),男,陕西咸阳人,硕士研究生,主要研究方向:图像处理;王芳梅(1984-),女,山东临沂人,硕士研究生,主要研究方向:图像处理。
  • 基金资助:

    国家自然科学基金资助项目(50875196, 60873119);陕西省自然科学基金资助项目(SJ08F17)

Recent advances in sparse representation of non-stationary signal

FAN Hong,GUO Peng,WANG Fang-mei   

  1. School of Computer Science, Shaanxi Normal University, Xi'an Shaanxi 710062, China
  • Received:2011-07-12 Revised:2011-09-25 Online:2012-02-06 Published:2012-01-01
  • Contact: FAN Hong

摘要: 信号分解是从信号中获取特征信息的过程,是模式识别、智能系统和故障诊断等诸多领域的基础和关键。非平稳信号往往包含着反映系统变化的重要信息,并且广泛存在,对其研究具有非常重要的理论意义和工程应用价值。以改进信号表示的稀疏性为主线,分析了推动非平稳信号特征提取方法发展的工程背景,详细描述了5类特征提取方法的特性与机理、历史沿革和面临的挑战,比较研究了各种方法的模型,并系统评述了这些模型在信号处理和分析中的最新进展,以及在一些领域中的应用。最后指出了各种方法目前存在的问题和不足,探讨了进一步的研究重点。

关键词: 非平稳信号, 信号分解, 稀疏性, 信号表示

Abstract: Signal decomposition is a process that obtains information from signals and it is a foundational and key technique for many fields such as pattern recognition, intelligent system and machinery fault diagnosis. It is very important to study non-stationary signal decomposition which always includes lots of information that can reflect the changing of the system and widely exists. After improving the sparsity of signal representation, the engineering background of feature extraction for non-stationary signal was studied in this paper, the characteristics, mechanisms, development history and current and future challenges of five types of methods were analyzed in depth, the models of these methods were compared, together with the state-of-the-art of feature extraction models in signal processing and analysis and some successful applications available were systematically reviewed. Finally, several main problems and a few deficiencies were pointed out, and future research directions were anticipated.

Key words: non-stationary signal, signal decomposition, sparsity, signal representation

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