基于高阶阈值函数与小波包的混沌信号降噪

doi:10.11772/j.issn.1001-9081.2014.04.0977

计算机应用 ›› 2014, Vol. 34 ›› Issue (4): 977-979.DOI: 10.11772/j.issn.1001-9081.2014.04.0977

基于高阶阈值函数与小波包的混沌信号降噪

杨杉,王建

四川大学锦城学院计算机科学与软件工程系,成都 611731

收稿日期:2013-09-16 修回日期:2013-11-09 出版日期:2014-04-01 发布日期:2014-04-29
通讯作者: 杨杉
作者简介:杨杉(1983-),女,四川成都人,讲师，博士研究生，主要研究方向:数字信号处理、数据挖掘;
王建(1979-),男,四川成都人,讲师，博士，主要研究方向:网络安全、数字信号处理。
基金资助:
国家自然科学基金青年科学基金资助项目

Noise reduction of chaotic signals based on high-order threshold function and wavelet packet

YANG Shan,WANG Jian

Department of Computer Science and Software Engineering, Jincheng College of Sichuan University, Chengdu Sichuan 611731, China

Received:2013-09-16 Revised:2013-11-09 Online:2014-04-01 Published:2014-04-29
Contact: YANG Shan

摘要/Abstract

摘要：

针对混沌信号小波降噪法中，高频段频率分辨率较差,且对小波分解系数所广泛采用的硬、软阈值量化方法存在着局限等问题，给出一种基于新型高阶阈值函数的混沌信号小波包降噪法。该方法采用小波包方法能够对小波分析中没有细分的高频部分进一步分解，保留了有用的高频信息，从而具有更加精确的局部分析能力；且所采用的阈值函数连续光滑，在噪声小波系数和混沌信号小波系数之间存在一个平滑过渡区，更符合信号的连续特性。仿真对比实验表明：与软阈值降噪法以及半软阈值与小波包降噪法相比，该方法对混沌信号的降噪效果明显，信噪比(SNR)有3.7~7dB的显著提高。

Abstract:

The conventional threshold function in wavelet noise reduction of chaotic signals has its shortages, such as low resolution of high frequency and restraint limit of quantitative method to hard and soft thresholds. Concerning these shortages, a wavelet packet noise reduction method of chaotic signals was proposed based on a high-order threshold function. The method could further decompose high frequency part by wavelet packet and retained useful high-frequency information, so it was more precious in partial analysis. Furthermore the threshold function was continuous and derivable. There was a smooth transiting area between noise wavelet coefficient and chaotic signal wavelet coefficient. As a result, it is more consistent with the continuous characteristics of the signal. The comparative simulation shows that, compared with soft threshold noise reduction method and semi-soft threshold wavelet packet noise reduction method, the effect of noise reduction to chaotic signals has been significantly improved, and Signal-to-Noise Ratio (SNR) increased 3.7-7dB.

中图分类号:

TN911.7

杨杉王建. 基于高阶阈值函数与小波包的混沌信号降噪[J]. 计算机应用, 2014, 34(4): 977-979.

YANG Shan WANG Jian. Noise reduction of chaotic signals based on high-order threshold function and wavelet packet[J]. Journal of Computer Applications, 2014, 34(4): 977-979.

参考文献

［1］WANG W, ZHANG X, WANG X. Noise reduction of chaotic signals based on independent component analysis and empirical mode decomposition ［J］. Acta Physica Sinica, 2013, 62(5): 506-515. (王文波,张晓东,汪祥莉.基于独立成分分析和经验模态分解的混沌信号降噪［J］.物理学报,2013,62(5):506-515.)
［2］DENG K, ZHANG L, LUO M K. Wavelet threshold method of resolving noise interference in periodic short-impulse signals chaotic detection ［J］. Chinese Physics B, 2010, 19(3): 536-543.
［3］LIU Y, YANG G, JIA Q. Adaptive noise reduction of chaotic signals based on dual-promotes wavelet ［J］. Acta Electronica Sinica, 2011, 39(1): 13-17. (刘云侠,杨国诗,贾群.基于双提升小波的自适应混沌信号降噪［J］.电子学报,2011,39(1):13-17.)
［4］LIU Y X, LIAO X W. Adaptive noise reduction for chaotic signals based on dual-lifting wavelet transform ［J］. Expert Systems with Applications: An International Journal, 2011, 38(3): 1346-1355.
［5］HAN M, LIU Y. Improved dual-wavelet spatial correlation method for denoising of chaotic signals ［J］. Journal of System Simulation, 2009, 21(15): 4743-4747. (韩敏,刘云侠.改进的双小波空域相关混沌信号降噪方法［J］.系统仿真学报, 2009, 21(15):4743-4747.)
［6］BAPTISTA M S, PEREIRA T, KURTHS J. Upper bounds in phase synchronous weak coherent chaotic attractors ［J］. Physica D: Nonlinear Phenomena, 2006, 216(2): 260-268.
［7］ZHANG W, XING B R. A duffing oscillator algorithm to detect the weak chromatographic signal ［J］. Analytica Chimica Acta, 2007, 585(1): 55-59.
［8］MAO M, XIAO Q, XIAO Y. Application of subspace noise reduction to warship chaotic signals ［J］. Applied Acoustics, 2009, 28(1): 37-41. (茆明,肖琼,肖毅.子空间降噪在舰船噪声混沌信号处理中的应用［J］.应用声学,2009,28(1):37-41.)
［9］LI C S, QU L S. Applications of chaotic oscillator in machinery fault diagnosis ［J］. Mechanical Systems and Signal Processing, 2007, 21(1): 257-269.
［10］WANG W, LI Q, ZHAO G J. Novel approach based on chaotic oscillator for machinery fault diagnosis ［J］. Measurement, 2008, 41(8): 904-911
［11］WANG J, XU X, ZHU G, et al.Noise reduction of chaotic signals by Markov model ［J］. Journal of System Simulation, 2009, 21(8): 2299-2302.(汪金菊,徐小红,朱功勤,等.混沌信号的马尔可夫模型降噪［J］.系统仿真学报，2009,21(8):2299-2302.)
［12］STEPHANE M. A wavelet tour of signal processing ［M］. New York: Academic, 1998.

[1]	欧跃发, 杨鸣坤, 慕德俊, 柯捷, 马文涛. 基于广义最大Versoria准则的稀疏自适应滤波算法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3325-3331.
[2]	廖列法李志明张赛赛. 基于深度残差网络的迭代量化哈希图像检索方法[J]. 计算机应用, 0, (): 0-0.
[3]	牛康力, 谌雨章, 沈君凤, 曾张帆, 潘永才, 王绎冲. 基于深度学习的双通道夜视图像复原方法[J]. 计算机应用, 2021, 41(6): 1775-1784.
[4]	夏玉杰, 时永鹏, 高雅, 孙鹏. 降低滤波器组多载波信号峰均比的边信息嵌入选择性映射方法[J]. 计算机应用, 2021, 41(5): 1425-1431.
[5]	杜年茂, 徐佳陈, 肖志勇. 基于卷积神经网络的欠采样脑部核磁共振图像重建方法[J]. 计算机应用, 2020, 40(10): 3060-3065.
[6]	孙建军, 徐岩. 基于加权改进模糊C均值聚类的欠定混合矩阵估计[J]. 计算机应用, 2020, 40(6): 1769-1773.
[7]	蒲杰官磊. 结合非局部相似性的foveated选票缺陷检测[J]. 计算机应用, 0, (): 0-0.
[8]	李维, 江虹, 伍春, 邓皓文. 改进环路结构的Gardner定时恢复算法[J]. 计算机应用, 2019, 39(10): 3013-3017.
[9]	陈龙彪, 谌雨章, 王晓晨, 邹鹏, 胡学敏. 基于深度学习的水下图像超分辨率重建方法[J]. 计算机应用, 2019, 39(9): 2738-2743.
[10]	王也, 黄国策, 董淑福. 短波通信中基于自适应帧长的数据速率变化改进算法[J]. 计算机应用, 2019, 39(8): 2386-2390.
[11]	史宝珠, 李美安. 基于角边特征的纸质碎片自动拼接复原算法[J]. 计算机应用, 2019, 39(2): 571-576.
[12]	洪睿, 康晓东, 郭军, 李博, 王亚鸽, 张秀芳. 基于复杂网络描述的图像深度卷积分类方法[J]. 计算机应用, 2018, 38(12): 3399-3402.
[13]	杜秀丽, 张薇, 陈波. 基于波浪式矩阵置换的稀疏度均衡分块压缩感知算法[J]. 计算机应用, 2018, 38(12): 3541-3546.
[14]	陈斌杰, 陆志华, 周宇, 叶庆卫. 基于双麦克风的室内语音分离与声源定位系统[J]. 计算机应用, 2018, 38(12): 3643-3648.
[15]	张刚, 高俊鹏. 组合型幂指函数三稳态随机共振微弱信号检测[J]. 计算机应用, 2018, 38(9): 2747-2752.

基于高阶阈值函数与小波包的混沌信号降噪

Noise reduction of chaotic signals based on high-order threshold function and wavelet packet

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics