基于改进的集成经验模态分解的海杂波去噪
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摘要
针对海杂波信号因混有噪声而难以提取的特点,提出基于改进的集成经验模态分解(MEEMD)的海杂波去噪方法。文中提出的MEEMD在补充的集成经验模态分解(CEEMD)的基础上,利用排列熵和Savitzky-Golay滤波对CEEMD分解后的固有模态函数进行处理,最后在经验模态分解分解重构后得到削噪后的信号。以IPIX雷达实测得到的海杂波数据进行仿真实验,结合最小二乘支持向量机建立混沌序列的单步预测模型,从预测误差中检测淹没在海杂波背景中的微弱信号,并用均方根误差判断去噪效果。仿真结果表明,文中所提出的MEEMD算法对模式混淆有很好的抑制效果,去噪后得到的均方根误差为0.000 847,比去噪前的均方根误差0.012 2降低了两个数量级。
In view of the characteristics of sea clutter signals which are difficult to extract due to mixed noises,an improved global mean empirical mode decomposition( MEEMD) based denoising method for sea clutter is proposed. The MEEMD proposed in this paper is based on the CEEMD decomposition and uses permutation entropy and Savitzky-Golay filtering to deal with it. Finally,the denoised signal is obtained after EMD decomposition and reconstruction. In order to get the IPIX radar sea clutter data simulation experiment,combined with least squares support vector machine to establish single chaotic sequence prediction model,the prediction errors from detection of weak signal submerged in the sea clutter wave in the background,and the root mean square error of judgment denoising effect. The simulation results show that the proposed MEEMD has good inhibition effect on mode confusion,the root mean square error after denoising was 0.000 847,0.012 2 more than the root mean square error of denoising before two orders of magnitude lower.
In view of the characteristics of sea clutter signals which are difficult to extract due to mixed noises,an improved global mean empirical mode decomposition( MEEMD) based denoising method for sea clutter is proposed. The MEEMD proposed in this paper is based on the CEEMD decomposition and uses permutation entropy and Savitzky-Golay filtering to deal with it. Finally,the denoised signal is obtained after EMD decomposition and reconstruction. In order to get the IPIX radar sea clutter data simulation experiment,combined with least squares support vector machine to establish single chaotic sequence prediction model,the prediction errors from detection of weak signal submerged in the sea clutter wave in the background,and the root mean square error of judgment denoising effect. The simulation results show that the proposed MEEMD has good inhibition effect on mode confusion,the root mean square error after denoising was 0.000 847,0.012 2 more than the root mean square error of denoising before two orders of magnitude lower.
引文
[1]HAYKIN S,LI X B.Detection of signals in chaos[J].Proceedings of the IEEE,1995,83(1):95-122.
[2]HAYKIN S,BAKKER R,CURRIE B W.Uncovering nonlinear dynamics-the case study of sea clutter[J].Proceedings of the IEEE,2002,90(5):860-881.
[3]FLANDRIN P,RILLING G,GONCALVES P.Empirical mode decomposition as a filter bank[J].IEEE Signal Processing Letters,2003,11(2):112-114.
[4]KURIAN A P,LEUNG H.Weak signal estimation in chaotic clutter using model-based coupled synchronization[J].IEEE Transactions on Circuits and Systems I:Regular Papers,2009,56(4):820-828.
[5]LI S X,LIU L Y.The study of denoising through wavelet shrinkage[J].Chinese Journal of Scientific Instrument,2002,23(3):478-479.
[6]徐晓刚,徐冠雷,王孝通,等.经验模式分解(EMD)及其应用[J].电子学报,2009,37(3):581-585.XU Xiaogang,XU Guanlei,WANG Xiaotong,et al.Empirical mode decomposition and its application[J].Acta Electronica Sinica,2009,37(3):581-585.
[7]行鸿彦,朱清清.基于集成经验模态分解的海杂波去噪[J].电子学报,2016,44(1):1-7.XING Hongyan,ZHU Qingqing.The sea clutter denoising based on ensemble empirical mode decomposition[J].Acta Electronica Sinica,2016,44(1):1-7.
[8]XING H Y,XU W.The neural networks method for detecting weak signals under chaotic background[J].Acta Physica Sinica,2007,56(7):3771-3776.
[9]HAYES B.A progress report on the fine art of turning literature into drivel[J].Computer Recreations,1983,249(5):18-28.
[10]GRASSBERGER P,PROCACCIA I.Measuring the strangeness of strange attractors[J].Physica D:Nonlinear Phenomena,1983,9(1-2):189-208.
[11]杨滨,杨晓伟,黄岚,等.自适应迭代最小二乘支持向量机回归算法[J].电子学报,2010,38(7):1621-1625.YANG Bin,YANG Xiaowei,HUANG Lan,et al.Adaptive and iterative training algorithm of least square support vector machine regression[J].Acta Electronica Sinica,2010,38(7):1621-1625.
[12]WU Z H,HUANG N E.Ensemble empirical mode decomposition:A noise assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41.
[13]YEH J R,SHIEH J S.Complementary ensemble empirical mode decomposition:A noise enhanced data analysis method[J].Advances in Adaptive Data Analysis,2010,2(2):135-156.
[14]BANDT C,POMPE B.Permutation entropy:a natural complexity measure for time series[J].Physical Review Letters,The American Physiological Society,2002,88(17):1-4.
[15]YAN R,GAO R X.Approximate entropy as a diagnostic tool for machine health monitoring[J].Mechanical Systems and Signal Processing,2007,21(2):824-839.
[16]赵雯雯,曾新雯.一种新的EMD去噪方法[J].电子科技,2008,21(5):30-32.ZHAO Wenwen,ZENG Xinwen.A new signal denoising method based on empirical mode decomposition(EMD)[J].Electronic Science and Technology,2008,21(5):30-32.
[17]郭凤霞,戚俊,陈斐楠,等.基于小波变换的声雷达模拟信号去噪研究[J].现代雷达,2016,38(3):86-90.GUO Fengxia,Qi Jun,CHEN Feinan,et al.A study on noise suppression methods for sodar analog signals based on wavelet transform[J].Modern Radar,2016,38(3):86-90.
[2]HAYKIN S,BAKKER R,CURRIE B W.Uncovering nonlinear dynamics-the case study of sea clutter[J].Proceedings of the IEEE,2002,90(5):860-881.
[3]FLANDRIN P,RILLING G,GONCALVES P.Empirical mode decomposition as a filter bank[J].IEEE Signal Processing Letters,2003,11(2):112-114.
[4]KURIAN A P,LEUNG H.Weak signal estimation in chaotic clutter using model-based coupled synchronization[J].IEEE Transactions on Circuits and Systems I:Regular Papers,2009,56(4):820-828.
[5]LI S X,LIU L Y.The study of denoising through wavelet shrinkage[J].Chinese Journal of Scientific Instrument,2002,23(3):478-479.
[6]徐晓刚,徐冠雷,王孝通,等.经验模式分解(EMD)及其应用[J].电子学报,2009,37(3):581-585.XU Xiaogang,XU Guanlei,WANG Xiaotong,et al.Empirical mode decomposition and its application[J].Acta Electronica Sinica,2009,37(3):581-585.
[7]行鸿彦,朱清清.基于集成经验模态分解的海杂波去噪[J].电子学报,2016,44(1):1-7.XING Hongyan,ZHU Qingqing.The sea clutter denoising based on ensemble empirical mode decomposition[J].Acta Electronica Sinica,2016,44(1):1-7.
[8]XING H Y,XU W.The neural networks method for detecting weak signals under chaotic background[J].Acta Physica Sinica,2007,56(7):3771-3776.
[9]HAYES B.A progress report on the fine art of turning literature into drivel[J].Computer Recreations,1983,249(5):18-28.
[10]GRASSBERGER P,PROCACCIA I.Measuring the strangeness of strange attractors[J].Physica D:Nonlinear Phenomena,1983,9(1-2):189-208.
[11]杨滨,杨晓伟,黄岚,等.自适应迭代最小二乘支持向量机回归算法[J].电子学报,2010,38(7):1621-1625.YANG Bin,YANG Xiaowei,HUANG Lan,et al.Adaptive and iterative training algorithm of least square support vector machine regression[J].Acta Electronica Sinica,2010,38(7):1621-1625.
[12]WU Z H,HUANG N E.Ensemble empirical mode decomposition:A noise assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41.
[13]YEH J R,SHIEH J S.Complementary ensemble empirical mode decomposition:A noise enhanced data analysis method[J].Advances in Adaptive Data Analysis,2010,2(2):135-156.
[14]BANDT C,POMPE B.Permutation entropy:a natural complexity measure for time series[J].Physical Review Letters,The American Physiological Society,2002,88(17):1-4.
[15]YAN R,GAO R X.Approximate entropy as a diagnostic tool for machine health monitoring[J].Mechanical Systems and Signal Processing,2007,21(2):824-839.
[16]赵雯雯,曾新雯.一种新的EMD去噪方法[J].电子科技,2008,21(5):30-32.ZHAO Wenwen,ZENG Xinwen.A new signal denoising method based on empirical mode decomposition(EMD)[J].Electronic Science and Technology,2008,21(5):30-32.
[17]郭凤霞,戚俊,陈斐楠,等.基于小波变换的声雷达模拟信号去噪研究[J].现代雷达,2016,38(3):86-90.GUO Fengxia,Qi Jun,CHEN Feinan,et al.A study on noise suppression methods for sodar analog signals based on wavelet transform[J].Modern Radar,2016,38(3):86-90.
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