• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Detection and classification of non-stationary signals using sparse representations in adaptive dictionaries.
【24h】

Detection and classification of non-stationary signals using sparse representations in adaptive dictionaries.

机译：使用自适应词典中的稀疏表示来检测和分类非平稳信号。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Automatic classification of non-stationary radio frequency (RF) signals is of particular interest in persistent surveillance and remote sensing applications. Such signals are often acquired in noisy, cluttered environments, and may be characterized by complex or unknown analytical models, making feature extraction and classification difficult. This thesis proposes an adaptive classification approach for poorly characterized targets and backgrounds based on sparse representations in non-analytical dictionaries learned from data. Conventional analytical orthogonal dictionaries, e.g., Short Time Fourier and Wavelet Transforms, can be suboptimal for classification of non-stationary signals, as they provide a rigid tiling of the time-frequency space, and are not specifically designed for a particular signal class. They generally do not lead to sparse decompositions (i.e., with very few non-zero coefficients), and use in classification requires separate feature selection algorithms. Pursuit-type decompositions in analytical overcomplete (non-orthogonal) dictionaries yield sparse representations, by design, and work well for signals that are similar to the dictionary elements. The pursuit search, however, has a high computational cost, and the method can perform poorly in the presence of realistic noise and clutter. One such overcomplete analytical dictionary method is also analyzed in this thesis for comparative purposes. The main thrust of the thesis is learning discriminative RF dictionaries directly from data, without relying on analytical constraints or additional knowledge about the signal characteristics. A pursuit search is used over the learned dictionaries to generate sparse classification features in order to identify time windows that contain a target pulse. Two state-of-the-art dictionary learning methods are compared, the K-SVD algorithm and Hebbian learning, in terms of their classification performance as a function of dictionary training parameters. Additionally, a novel hybrid dictionary algorithm is introduced, demonstrating better performance and higher robustness to noise. The issue of dictionary dimensionality is explored and this thesis demonstrates that undercomplete learned dictionaries are suitable for non-stationary RF classification. Results on simulated data sets with varying background clutter and noise levels are presented. Lastly, unsupervised classification with undercomplete learned dictionaries is also demonstrated in satellite imagery analysis.
机译：在固定监视和遥感应用中，非固定射频（RF）信号的自动分类特别受关注。此类信号通常是在嘈杂，杂乱的环境中获取的，并且可能具有复杂或未知的分析模型，从而使特征提取和分类变得困难。本文提出了一种自适应分类方法，该方法基于从数据中学到的非分析词典中的稀疏表示，来对特征差的目标和背景进行自适应分类。常规的分析正交字典，例如短时傅立叶和小波变换，对于非平稳信号的分类可能不是最佳的，因为它们提供了时频空间的刚性叠加，并且不是专门为特定信号类别设计的。它们通常不会导致稀疏分解（即具有非常少的非零系数），并且在分类中使用需要单独的特征选择算法。通过设计，分析超完备（非正交）词典中的追踪类型分解会产生稀疏表示，并且对于类似于字典元素的信号也能很好地工作。然而，追踪搜索具有较高的计算成本，并且该方法在存在真实的噪声和混乱的情况下性能可能较差。出于比较目的，本文还分析了一种这样的超完备分析词典方法。本文的主要目的是直接从数据中学习区分性RF词典，而无需依赖分析约束或关于信号特性的其他知识。追踪搜索用于学习的字典，以生成稀疏分类特征，以识别包含目标脉冲的时间窗。比较了两种最先进的字典学习方法，即K-SVD算法和Hebbian学习，它们的分类性能是字典训练参数的函数。此外，引入了一种新颖的混合字典算法，展示了更好的性能和对噪声的更高鲁棒性。探讨了字典维数的问题，证明了学习不足的字典适用于非平稳的RF分类。给出了具有不同背景杂波和噪声水平的模拟数据集的结果。最后，在卫星图像分析中还展示了学习字典不完整的无监督分类。

著录项

  • 作者

    Moody, Daniela I.;

  • 作者单位

    University of Maryland, College Park.;

  • 授予单位 University of Maryland, College Park.;
  • 学科 Engineering Electronics and Electrical.;Remote Sensing.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 222 p.
  • 总页数 222
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号