...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Information Theory, IEEE Transactions on >Compressive Spectral Estimation for Nonstationary Random Processes
【24h】

Compressive Spectral Estimation for Nonstationary Random Processes

机译：非平稳随机过程的压缩谱估计

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

Estimating the spectral characteristics of a nonstationary random process is an important but challenging task, which can be facilitated by exploiting structural properties of the process. In certain applications, the observed processes are underspread, i.e., their time and frequency correlations exhibit a reasonably fast decay, and approximately time-frequency sparse, i.e., a reasonably large percentage of the spectral values are small. For this class of processes, we propose a compressive estimator of the discrete Rihaczek spectrum (RS). This estimator combines a minimum variance unbiased estimator of the RS (which is a smoothed Rihaczek distribution using an appropriately designed smoothing kernel) with a compressed sensing technique that exploits the approximate time-frequency sparsity. As a result of the compression stage, the number of measurements required for good estimation performance can be significantly reduced. The measurements are values of the ambiguity function of the observed signal at randomly chosen time and frequency lag positions. We provide bounds on the mean-square estimation error of both the minimum variance unbiased RS estimator and the compressive RS estimator, and we demonstrate the performance of the compressive estimator by means of simulation results. The proposed compressive RS estimator can also be used for estimating other time-dependent spectra (e.g., the Wigner–Ville spectrum), since for an underspread process most spectra are almost equal.
机译：估计非平稳随机过程的光谱特性是一项重要但具有挑战性的任务，可以通过利用该过程的结构特性来简化此过程。在某些应用中，所观察到的过程未充分扩展，即它们的时间和频率相关性表现出相当快的衰减，并且近似时频稀疏，即相当大的频谱值百分比很小。对于此类过程，我们提出了离散Rihaczek频谱（RS）的压缩估计器。该估计器将RS的最小方差无偏估计器（使用适当设计的平滑内核进行平滑的Rihaczek分布）与利用近似时频稀疏性的压缩传感技术相结合。压缩阶段的结果是，可以显着减少获得良好估算性能所需的测量次数。测量值是在随机选择的时间和频率滞后位置处观察到的信号的模糊度函数的值。我们提供了最小方差无偏RS估计器和压缩RS估计器的均方估计误差的界限，并通过仿真结果证明了压缩估计器的性能。所提出的压缩RS估计器也可以用于估计其他与时间相关的光谱（例如Wigner-Ville光谱），因为对于欠扩展过程，大多数光谱几乎相等。

著录项

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