公开(公告)号：WO2017205382A1

公开(公告)日：2017-11-30

申请号：PCT/US2017/034017

申请日：2017-05-23

Applicant: THE UNIVERSITY OF NEW HAMPSHIRE

Inventor： KIRSCH, Nicholas J. ,  AL-BADRAWI, Mahdi H. ,  AL-JEWAD, Bessam Z.

IPC: G06F17/14 ,  G06T7/00 ,  G10L15/20 ,  G10L19/02 ,  G10L21/02 ,  H04L27/00

CPC classification number: G10L21/0232 ,  G06F17/14 ,  G06N7/005 ,  G10L15/20 ,  G10L21/0216 ,  G10L21/0264

Abstract: Techniques for EMD-based signal de-noising are disclosed that use statistical characteristics of IMFs to identify information-carrying IMFs for the purposes of partially reconstructing the identified relevant IMFs into a de-noised signal. The present disclosure has identified that the statistical characteristics of IMFs with noise tend to follow a generalized Gaussian distribution (GGD) versus only a Gaussian or Laplace distribution. Accordingly, a framework for relevant IMF selection is disclosed that includes, in part, performing a null hypothesis test against a distribution of each IMF derived from the use of a generalized probability density function (PDF). IMFs that contribute more noise than signal may thus be identified through the null hypothesis test. Conversely, the aspects and embodiments disclosed herein enable the determination of which IMFs have a contribution of more signal than noise. Thus, a signal may be partially reconstructed based on the predominately information-carrying IMFs to result in de-noised output signal.

Abstract translation: 公开了用于基于EMD的信号去噪的技术，其使用IMF的统计特性来识别信息承载的IMF，用于将所识别的相关IMF部分重构为去噪信号。 本公开已经识别出具有噪声的IMF的统计特性倾向于遵循广义高斯分布（GGD）而不是高斯或拉普拉斯分布。 因此，公开了用于相关IMF选择的框架，其部分地包括对由使用广义概率密度函数（PDF）导出的每个IMF的分布执行零假设测试。 因此可以通过零假设检验来识别比信号贡献更多噪声的IMF。 相反，本文公开的方面和实施例使得能够确定哪些IMF具有比噪声更多的信号的贡献。 因此，可以基于主要携带信息的IMF来部分重构信号以导致降噪输出信号。

公开(公告)号：WO2018053525A1

公开(公告)日：2018-03-22

申请号：PCT/US2017/052302

申请日：2017-09-19

Applicant: THE UNIVERSITY OF NEW HAMPSHIRE

Inventor： AL-BADRAWI, Mahdi H. ,  KIRSCH, Nicholas J. ,  AL-JEWAD, Bessam Z.

IPC: G01L21/02

Abstract: An Empirical Mode Decomposition (EMD)-based noise estimation process is disclosed herein that allows for blind estimations of noise power for a given signal under test. The EMD-based noise estimation process is non-parametric and adaptive to a signal, which allows the EMD-based noise estimation process to operate without necessarily having a priori knowledge about the received signal. Existing approaches to spectrum sensing such as Energy Detector (ED) and Maximum Eigenvalue Detector (MED), for example, may be modified to utilize a EMD-based noise estimation process consistent with the present disclosure to shift the same from semi-blind category to fully-blind category.

Abstract translation: 本文公开了基于经验模式分解（EMD）的噪声估计过程，其允许针对给定被测信号的噪声功率的盲估计。 基于EMD的噪声估计过程是非参数的并且对信号是自适应的，这使得基于EMD的噪声估计过程不需要具有关于接收到的信号的先验知识即可操作。 例如，现有的频谱感测方法（例如能量检测器（ED）和最大特征值检测器（MED））可以被修改以利用与本公开一致的基于EMD的噪声估计过程将其从半盲类别转移到 完全盲目的类别。