搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

混沌信号自适应协同滤波去噪

王梦蛟 周泽权 李志军 曾以成

引用本文:
Citation:

混沌信号自适应协同滤波去噪

王梦蛟, 周泽权, 李志军, 曾以成

An adaptive denoising algorithm for chaotic signals based on collaborative filtering

Wang Meng-Jiao, Zhou Ze-Quan, Li Zhi-Jun, Zeng Yi-Cheng
PDF
导出引用
  • 混沌信号协同滤波去噪算法充分利用了混沌信号的自相似结构特征,具有良好的信噪比提升性能.针对该算法的滤波参数优化问题,考虑到最优滤波参数的选取受到信号特征、采样频率和噪声水平的影响,为提高该算法的自适应性使其更符合实际应用需求,基于排列熵提出一种滤波参数自动优化准则.依据不同噪声水平的混沌信号排列熵的不同,首先选取不同滤波参数对含噪混沌信号进行去噪,然后计算各滤波参数对应重构信号的排列熵,最后通过比较各重构信号的排列熵,选取排列熵最小的重构信号对应的滤波参数为最优滤波参数,实现滤波参数的优化.分析了不同信号特征、采样频率和噪声水平情况下滤波参数的选取规律.仿真结果表明,该参数优化准则能在不同条件下对滤波参数进行有效的自动最优化,提高了混沌信号协同滤波去噪算法的自适应性.
    Chaos is a seemingly random and irregular movement, happening in a deterministic system without random factors. Chaotic theory has promising applications in various areas (e.g., communication, image encryption, geophysics, weak signal detection). However, observed chaotic signals are often contaminated by noise. The presence of noise hinders the chaos theory from being applied to related fields. Therefore, it is important to develop a new method of suppressing the noise of the chaotic signals. Recently, the denoising algorithm for chaotic signals based on collaborative filtering was proposed. Its denoising performance is better than those of the existing denoising algorithms for chaotic signals. The denoising algorithm for chaotic signals based on collaborative filtering makes full use of the self-similar structural feature of chaotic signals. However, in the parameter optimization issue of the denoising algorithm, the selection of the filter parameters is affected by signal characteristic, sampling frequency and noise level. In order to improve the adaptivity of the denoising algorithm, a criterion for selecting the optimal filter parameters is proposed based on permutation entropy in this paper. The permutation entropy can effectively measure the complexity of time series. It has been widely applied to physical, medical, engineering, and economic sciences. According to the difference among the permutation entropies of chaotic signals at different noise levels, first, different filter parameters are used for denoising noisy chaotic signals. Then, the permutation entropy of the reconstructed chaotic signal corresponding to each of filter parameters is computed. Finally, the permutation entropies of the reconstructed chaotic signals are compared with each other, and the filter parameter corresponding to the minimum permutation entropy is selected as an optimal filter parameter. The selections of the filter parameters are analyzed in the cases of different signal characteristics, different sampling frequencies and different noise levels. Simulation results show that this criterion can automatically optimize the filter parameter efficiently in different conditions, which improves the adaptivity of the denoising algorithm for chaotic signals based on collaborative filtering.
      通信作者: 王梦蛟, wangmj@xtu.edu.cn
    • 基金项目: 国家自然科学基金(批准号:61471310,11747087)、湖南省教育厅科学研究基金(批准号:17C1530)和湘潭大学自然科学基金(批准号:15XZX33)资助的课题.
      Corresponding author: Wang Meng-Jiao, wangmj@xtu.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 61471310, 11747087), the Research Foundation of Education Bureau of Hunan Province, China (Grant No. 17C1530), and the Natural Science Foundation of Xiangtan University, China (Grant No. 15XZX33).
    [1]

    L J H, Lu J A, Chen S H 2002 The Analysis and Applications of Chaotic Time Series (Wuhan:Wuhan University Press) pp1-8 (in Chinese) [吕金虎, 陆君安, 陈士华 2002 混沌时间序列分析及其应用(武汉:武汉大学出版社)第1–8页]

    [2]

    Han M, Xu M L 2013 Acta Phys. Sin. 62 120510 (in Chinese) [韩敏, 许美玲 2013 物理学报 62 120510]

    [3]

    Sun J W, Shen Y, Yin Q, Xu C J 2013 Chaos 23 013140

    [4]

    Li G Z, Zhang B 2017 IEEE Trans. Ind. Electron. 64 2255

    [5]

    Peng G Y, Min F H 2017 Nonlinear Dynam. 90 1607

    [6]

    Urbanowicz K, Hołyst J A 2003 Phys. Rev. E 67 046218

    [7]

    Feng J C 2012 Chaotic Signals and Information Processing (Beijing:Tsinghua University Press) pp32-35 (in Chinese) [冯久超 2012 混沌信号与信息处理(北京:清华大学出版社)第32–35页]

    [8]

    Badii R, Broggi G, Derighetti B, Ravani M 1988 Phys. Rev. Lett. 60 979

    [9]

    Cawley R, Hsu G H 1992 Phys. Rev. A 46 3057

    [10]

    Schreiber T, Richter M 1999 Int. J. Bifurcat. Chaos 9 2039

    [11]

    Donoho D L 1995 IEEE Trans. Inf. Theory 41 613

    [12]

    Han M, Liu Y H, Xi J H, Guo W 2007 IEEE Signal Process. Lett. 14 62

    [13]

    Kopsinis Y, McLaughlin S 2009 IEEE Trans. Signal Process. 57 1351

    [14]

    Wang W B, Zhang X D, Wang X L 2013 Acta Phys. Sin. 62 050201 (in Chinese) [王文波, 张晓东, 汪祥莉 2013 物理学报 62 050201]

    [15]

    Tung W W, Gao J B, Hu J, Yang L 2011 Phys. Rev. E 83 046210

    [16]

    Gao J B, Sultan H, Hu J, Tung W W 2010 IEEE Signal Process. Lett. 17 237

    [17]

    Chen Y, Liu X Y, Wu Z T, Fan Y, Ren Z L, Feng J C 2017 Acta Phys. Sin. 66 210501 (in Chinese) [陈越, 刘雄英, 吴中堂, 范艺, 任子良, 冯久超 2017 物理学报 66 210501]

    [18]

    Dabov K, Foi A, Katkovnik V, Egiazarian K 2007 IEEE Trans. Image Process. 16 2080

    [19]

    Yadav S K, Sinha R, Bora P K 2015 IET Signal Process. 9 88

    [20]

    Hou W, Feng G L, Dong W J, Li J P 2006 Acta Phys. Sin. 55 2663 (in Chinese) [侯威, 封国林, 董文杰, 李建平 2006 物理学报 55 2663]

    [21]

    Sun K H, He S B, Yin L Z, A D L·Duo L K 2012 Acta Phys. Sin. 61 130507 (in Chinese) [孙克辉, 贺少波, 尹林子, 阿地力·多力坤 2012 物理学报 61 130507]

    [22]

    Yu M Y, Sun K H, Liu W H, He S B 2018 Chaos Solitons Fractals 106 107

    [23]

    Donoho D L, Johnstone I M 1994 Biometrika 81 425

    [24]

    He S B, Sun K H, Wang H H 2016 Physical A 461 812

    [25]

    Bandt C, Pompe B 2002 Phys. Rev. Lett. 88 174102

    [26]

    Lorenz E N 1963 J. Atmos. Sci. 20 130

    [27]

    Chen G R, Ueta T 1999 Int. J. Bifurcat. Chaos 9 1465

  • [1]

    L J H, Lu J A, Chen S H 2002 The Analysis and Applications of Chaotic Time Series (Wuhan:Wuhan University Press) pp1-8 (in Chinese) [吕金虎, 陆君安, 陈士华 2002 混沌时间序列分析及其应用(武汉:武汉大学出版社)第1–8页]

    [2]

    Han M, Xu M L 2013 Acta Phys. Sin. 62 120510 (in Chinese) [韩敏, 许美玲 2013 物理学报 62 120510]

    [3]

    Sun J W, Shen Y, Yin Q, Xu C J 2013 Chaos 23 013140

    [4]

    Li G Z, Zhang B 2017 IEEE Trans. Ind. Electron. 64 2255

    [5]

    Peng G Y, Min F H 2017 Nonlinear Dynam. 90 1607

    [6]

    Urbanowicz K, Hołyst J A 2003 Phys. Rev. E 67 046218

    [7]

    Feng J C 2012 Chaotic Signals and Information Processing (Beijing:Tsinghua University Press) pp32-35 (in Chinese) [冯久超 2012 混沌信号与信息处理(北京:清华大学出版社)第32–35页]

    [8]

    Badii R, Broggi G, Derighetti B, Ravani M 1988 Phys. Rev. Lett. 60 979

    [9]

    Cawley R, Hsu G H 1992 Phys. Rev. A 46 3057

    [10]

    Schreiber T, Richter M 1999 Int. J. Bifurcat. Chaos 9 2039

    [11]

    Donoho D L 1995 IEEE Trans. Inf. Theory 41 613

    [12]

    Han M, Liu Y H, Xi J H, Guo W 2007 IEEE Signal Process. Lett. 14 62

    [13]

    Kopsinis Y, McLaughlin S 2009 IEEE Trans. Signal Process. 57 1351

    [14]

    Wang W B, Zhang X D, Wang X L 2013 Acta Phys. Sin. 62 050201 (in Chinese) [王文波, 张晓东, 汪祥莉 2013 物理学报 62 050201]

    [15]

    Tung W W, Gao J B, Hu J, Yang L 2011 Phys. Rev. E 83 046210

    [16]

    Gao J B, Sultan H, Hu J, Tung W W 2010 IEEE Signal Process. Lett. 17 237

    [17]

    Chen Y, Liu X Y, Wu Z T, Fan Y, Ren Z L, Feng J C 2017 Acta Phys. Sin. 66 210501 (in Chinese) [陈越, 刘雄英, 吴中堂, 范艺, 任子良, 冯久超 2017 物理学报 66 210501]

    [18]

    Dabov K, Foi A, Katkovnik V, Egiazarian K 2007 IEEE Trans. Image Process. 16 2080

    [19]

    Yadav S K, Sinha R, Bora P K 2015 IET Signal Process. 9 88

    [20]

    Hou W, Feng G L, Dong W J, Li J P 2006 Acta Phys. Sin. 55 2663 (in Chinese) [侯威, 封国林, 董文杰, 李建平 2006 物理学报 55 2663]

    [21]

    Sun K H, He S B, Yin L Z, A D L·Duo L K 2012 Acta Phys. Sin. 61 130507 (in Chinese) [孙克辉, 贺少波, 尹林子, 阿地力·多力坤 2012 物理学报 61 130507]

    [22]

    Yu M Y, Sun K H, Liu W H, He S B 2018 Chaos Solitons Fractals 106 107

    [23]

    Donoho D L, Johnstone I M 1994 Biometrika 81 425

    [24]

    He S B, Sun K H, Wang H H 2016 Physical A 461 812

    [25]

    Bandt C, Pompe B 2002 Phys. Rev. Lett. 88 174102

    [26]

    Lorenz E N 1963 J. Atmos. Sci. 20 130

    [27]

    Chen G R, Ueta T 1999 Int. J. Bifurcat. Chaos 9 1465

  • [1] 刘远, 袁冀扬, 周心雨, 谷双全, 周沛, 穆鹏华, 李念强. 基于滤波反馈宽带平坦混沌信号的快速物理随机比特产生. 物理学报, 2022, 71(22): 224203. doi: 10.7498/aps.71.20221173
    [2] 汪书潮, 苏秀琴, 朱文华, 陈松懋, 张振扬, 徐伟豪, 王定杰. 基于弹性变分模态提取的时间相关单光子计数信号去噪. 物理学报, 2021, 70(17): 174304. doi: 10.7498/aps.70.20210149
    [3] 刘柏年, 皇群博, 张卫民, 任开军, 曹小群, 赵军. 集合背景误差方差中小波阈值去噪方法研究及试验. 物理学报, 2017, 66(2): 020505. doi: 10.7498/aps.66.020505
    [4] 陈越, 刘雄英, 吴中堂, 范艺, 任子良, 冯久超. 受污染混沌信号的协同滤波降噪. 物理学报, 2017, 66(21): 210501. doi: 10.7498/aps.66.210501
    [5] 胡进峰, 张亚璇, 李会勇, 杨淼, 夏威, 李军. 基于最优滤波器的强混沌背景中谐波信号检测方法研究. 物理学报, 2015, 64(22): 220504. doi: 10.7498/aps.64.220504
    [6] 李雄杰, 周东华. 一种基于强跟踪滤波的混沌保密通信方法. 物理学报, 2015, 64(14): 140501. doi: 10.7498/aps.64.140501
    [7] 李广明, 吕善翔. 混沌信号的压缩感知去噪. 物理学报, 2015, 64(16): 160502. doi: 10.7498/aps.64.160502
    [8] 王梦蛟, 吴中堂, 冯久超. 一种参数优化的混沌信号自适应去噪算法. 物理学报, 2015, 64(4): 040503. doi: 10.7498/aps.64.040503
    [9] 谢映海, 杨维, 张玉. 离散空间上的最小能量框架及其在矩形脉冲信号去噪中的应用研究. 物理学报, 2010, 59(11): 8255-8263. doi: 10.7498/aps.59.8255
    [10] 王国光, 王丹, 何丽桥. 混沌中信号的投影滤波. 物理学报, 2010, 59(5): 3049-3056. doi: 10.7498/aps.59.3049
    [11] 杜杰, 曹一家, 刘志坚, 徐立中, 江全元, 郭创新, 陆金桂. 混沌时间序列的局域高阶Volterra滤波器多步预测模型. 物理学报, 2009, 58(9): 5997-6005. doi: 10.7498/aps.58.5997
    [12] 王云才, 李艳丽, 王安帮, 王冰洁, 张耕玮, 郭 萍. 激光混沌通信中半导体激光器接收机对高频信号的滤波特性. 物理学报, 2007, 56(8): 4686-4693. doi: 10.7498/aps.56.4686
    [13] 刘新元, 谢柏青, 戴远东, 王福仁, 李壮志, 马 平, 谢飞翔, 杨 涛, 聂瑞娟. 射频SQUID心磁图数据自适应滤波研究. 物理学报, 2005, 54(4): 1937-1942. doi: 10.7498/aps.54.1937
    [14] 张家树, 李恒超, 肖先赐. 连续混沌信号的离散余弦变换域二次实时滤波预测. 物理学报, 2004, 53(3): 710-716. doi: 10.7498/aps.53.710
    [15] 赵 莉, 陈赓华, 张利华, 黄旭光, 翟光杰, 李俊文, 汤玉林, 冯 稷. 互补型自适应滤波器在心磁信号处理中的应用. 物理学报, 2004, 53(12): 4420-4427. doi: 10.7498/aps.53.4420
    [16] 闵富红, 须文波, 徐振源. 用多凹槽滤波器控制混沌系统. 物理学报, 2002, 51(8): 1690-1695. doi: 10.7498/aps.51.1690
    [17] 韦保林, 罗晓曙, 汪秉宏, 全宏俊, 郭维, 傅金阶. 一种基于三阶Volterra滤波器的混沌时间序列自适应预测方法. 物理学报, 2002, 51(10): 2205-2210. doi: 10.7498/aps.51.2205
    [18] 张家树, 肖先赐. 用于混沌时间序列自适应预测的一种少参数二阶Volterra滤波器. 物理学报, 2001, 50(7): 1248-1254. doi: 10.7498/aps.50.1248
    [19] 张家树, 肖先赐. 用一种少参数非线性自适应滤波器自适应预测低维混沌时间序列. 物理学报, 2000, 49(12): 2333-2339. doi: 10.7498/aps.49.2333
    [20] 张家树, 肖先赐. 混沌时间序列的自适应高阶非线性滤波预测. 物理学报, 2000, 49(7): 1221-1227. doi: 10.7498/aps.49.1221
计量
  • 文章访问数:  6286
  • PDF下载量:  245
  • 被引次数: 0
出版历程
  • 收稿日期:  2017-11-17
  • 修回日期:  2018-01-06
  • 刊出日期:  2019-03-20

/

返回文章
返回