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A hybrid ICA-SVM approach to automatic modulation recognition.

机译：一种用于自动调制识别的混合ICA-SVM方法。

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摘要

Automatic modulation recognition is a topic of interest in many fields including signal surveillance, multi-user detection and radio frequency spectrum monitoring. This thesis examines several issues related to automatic modulation recognition and presents an alternative low SNR recognition algorithm using elements of cyclostationary analysis, independent component analysis and support vector machines. Previous work by A.K. Nandi, E.E. Azzouz and A. Swami use instantaneous signal measurements or high order cummulants to identify modulations of interest. A major weakness of conventional modulation recognition algorithms is their sensitivity to SNR variations. In addition to this; the majority of research into this area does not consider continuous phase modulated signals or the effect of channel memory on algorithm performance. The hybrid algorithm developed in this thesis is applied to both digital and continuous phase modulated signals under a variety of channel impairments.;By deploying an independent component analysis algorithm on the cyclic feature sets, a pseudo-distance measure is shown to be increased. By increasing this pseudo-distance measure, the distance between modulation subspaces in the support vector machine hypothesis space is also increased. By increasing the distance between modulation subspaces the corresponding number of classification errors are decreased. Several empirical examples are provided to illustrate this connection. In addition to the modulation recognition problem the algorithm is applied to a different problem involving engine classification. Using a modified cepstral feature set; the hybrid algorithm is able to distinguish different engine types operating in different modes based upon vibrational and acoustic data.;The algorithm presented, based upon the novel combination of independent component analysis and support vector machines, is able to reliably classify a wide array of modulation types in much more unfavorable signal environments than previous approaches. Furthermore, the favorable results obtained by applying the algorithm to a separate recognition problem indicate a wider area of application than only modulation recognition.
机译：自动调制识别是许多领域中令人关注的主题，包括信号监视，多用户检测和射频频谱监视。本文研究了与自动调制识别相关的几个问题，并提出了一种使用循环平稳分析，独立分量分析和支持向量机的元素的低信噪比识别算法。 A.K.的先前作品Nandi，E.E。Azzouz和A.Swami使用瞬时信号测量或高阶累积量来识别感兴趣的调制。常规调制识别算法的主要缺点是它们对SNR变化的敏感性。除此之外;该领域的大多数研究都没有考虑连续相位调制信号或信道存储器对算法性能的影响。本文开发的混合算法适用于多种信道损伤下的数字和连续相位调制信号。通过在循环特征集上部署独立的分量分析算法，伪距离度量得到了提高。通过增加该伪距离度量，支持向量机假设空间中调制子空间之间的距离也增加了。通过增加调制子空间之间的距离，减少了相应的分类误差数量。提供了一些经验示例来说明这种连接。除了调制识别问题外，该算法还应用于涉及发动机分类的其他问题。使用修改后的倒谱特征集；该混合算法能够基于振动和声学数据来区分以不同模式运行的不同发动机类型。基于独立分量分析和支持向量机的新颖组合，提出的算法能够可靠地对各种调制进行分类比以前的方法更不利于信号环境。此外，通过将算法应用于单独的识别问题而获得的良好结果表明，与仅调制识别相比，其应用范围更广。

著录项

  • 作者

    Boutte, David.;

  • 作者单位

    The University of New Mexico.;

  • 授予单位 The University of New Mexico.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 104 p.
  • 总页数 104
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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