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首页> 外文会议>International Conference on Computational Science and Its Applications(ICCSA 2006) pt.1; 20060508-11; Glasgow(GB) >Noise Level Estimation Using Haar Wavelet Packet Trees for Sensor Robust Outlier Detection
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Noise Level Estimation Using Haar Wavelet Packet Trees for Sensor Robust Outlier Detection

机译：使用Haar小波包树的噪声水平估计用于传感器鲁棒异常值检测

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摘要

The paper is related to the on-line noise variance estimation. In practical use, it is important to estimate the noise level from the data rather than to assume that the noise level is known. The paper presented a free thresholding method related to the on-line peak noise variance estimation even for signal with small S/N ratio. The basic idea is to characterize the noise like an incoherent part of the measured signal. This is performed through the wavelet tree by choosing the subspaces where the median value of the wavelet components has minimum. The paper provides to show nice general properties of the wavelet packets on which the proposed procedure is based. The developed algorithm is totally general even though is applied by using Haar wavelet packets and it is present in some industrial software platforms to detect sensor outliers. More, it is currently integrated in the inferential modeling platform of the Advanced Control and Simulation Solution Responsible Unit within ABB's industry division.
机译：本文涉及在线噪声方差估计。在实际使用中，重要的是根据数据估算噪声水平，而不是假设噪声水平已知。提出了一种与在线峰值噪声方差估计有关的免费阈值方法，即使对于信噪比较小的信号也是如此。基本思想是将噪声表征为被测信号的非相干部分。通过选择子空间中小波分量中值最小的子空间，可以通过小波树执行此操作。该文件提供了展示所提出的过程所基于的小波包的良好一般特性。即使通过使用Haar小波包应用了该算法，该算法也是完全通用的，并且它已存在于某些工业软件平台中，用于检测传感器异常值。此外，它目前已集成在ABB行业部门高级控制和仿真解决方案负责人的推理建模平台中。

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