一類非線性信號(hào)去噪的奇異值分解有效迭代方法

查翔; 倪世宏; 張鵬

doi:10.11999/JEIT141605

一類非線性信號(hào)去噪的奇異值分解有效迭代方法

doi: 10.11999/JEIT141605

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61372167, 61379104)資助課題

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- 文章訪問(wèn)數(shù): 1497
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出版歷程
- 收稿日期: 2014-12-15
- 修回日期: 2015-03-05
- 刊出日期: 2015-06-19

Effective Iteration Method of a Class of Nonlinear Signal Denoising Based on Singular Value Decomposition

摘要

摘要: 對(duì)于一類非線性信號(hào)的去噪問(wèn)題，該文提出一種基于奇異值分解(Singular Value Decomposition, SVD)的有效迭代方法。對(duì)現(xiàn)有奇異值差分譜方法在兩類不同非線性信號(hào)上的去噪效果進(jìn)行了對(duì)比，指出在信號(hào)不具有明顯特征頻率、非周期性變化時(shí)這一方法并不適用，并分析了現(xiàn)象產(chǎn)生的原因；然后針對(duì)該類信號(hào)的特點(diǎn)重新定義了Hankel矩陣結(jié)構(gòu)，給出有效奇異值的確定方式，并通過(guò)SVD多次迭代過(guò)程實(shí)現(xiàn)對(duì)該類信號(hào)的有效去噪。對(duì)實(shí)際飛行數(shù)據(jù)去噪的實(shí)驗(yàn)結(jié)果表明，該方法對(duì)提出的一類信號(hào)對(duì)象不僅去噪效果良好，而且可提高運(yùn)算效率。
- 信號(hào)處理 /
- 去噪 /
- 奇異值分解 /
- 特征頻率 /
- Hankel矩陣
Abstract: To solve a class of nonlinear signal denoising, an effective iteration method based on the Singular Value Decomposition (SVD) is proposed. When the signals have no obvious characteristic frequency and non-periodic change, the current difference spectrum method is not applicable by comparing the results on the two class of nonlinear signal, and then the corresponding reason is analyzed. According to the signal feature, the structure of the Hankel matrix is defined again and the valid singular values are determined. The effective denoising is realized by the repeated iteration which is based on the SVD. The results of the flight data demonstrate that the proposed method can effectively reduce the noise and improve the computing efficiency as well.
- Signal processing /
- Denoising /
- Singular Value Decomposition (SVD) /
- Characteristic frequency /
- Hankel matrix

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參考文獻(xiàn)(19)

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