您好,欢迎来到中国测试科技资讯平台!

首页> 《中国测试》期刊 >本期导读>一种非线性Chirp信号模态分解算法研究

一种非线性Chirp信号模态分解算法研究

1552    2020-10-27

免费

全文售价

作者:句海洋1, 王新华1, 金浩2, 潘长城2

作者单位:1. 北京工业大学机械工程与应用电子技术学院,北京 100124;
2. 中国船舶重工集团公司第七一四研究所,北京 100101


关键词:非线性信号;模态分解;解调技术;宽带信号;交替方向乘子法


摘要:

在实际问题中,非线性Chirp信号模态分量提取问题一直是信号处理领域的难点之一,因此,该文通过理论分析与仿真验证,提出一种非线性Chirp信号模态分解算法。该算法采用解调技术将宽带信号转换为窄带信号,然后,将分解问题表述为一个解调问题的最优求解过程,进而采用交替方向乘子法和反解调算子联合分析得到全局最优解。通过仿真信号分解结果表明,所提取的分量频率绝对误差不超过0.02 Hz,分量的幅值波动相对误差小于1%;进一步研究该算法在不同信噪比情况下的估计信号的均方根误差(RMSE)和二范数(L2),在信噪比大于12 dB时,误差在可接受范围内,在信噪比大于20 dB时,RMSE和L2趋于稳定下降状态。通过分析不同信噪比下的仿真信号,验证该文所提出算法的正确性,可为非线性Chirp信号的模态分解提供一种新方法,具备一定的应用价值。


Research on mode decomposition algorithm of nonlinear Chirp signal
JU Haiyang1, WANG Xinhua1, JIN Hao2, PAN Changcheng2
1. College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China;
2. The 714 Research Institute of CSIC, Beijing 100101, China
Abstract: In practice, the problem of modal component extraction of nonlinear chirp signal is one of the difficulties in the field of signal processing. Therefore, we propose a nonlinear chirp mode decomposition algorithm through theoretical analysis and simulation verification. The algorithm converts a wide-band signal into a narrow-band signal by using a demodulation technique, and then decomposition problem is expressed as an optimal solution process of demodulation problem. Furthermore, global optimal solution is obtained by the joint analysis of alternating direction method of multipliers and backward modulation operator. Decomposition results of nonlinear chirp simulation signals show that the absolute error of extracted component frequency does not exceed 0.02 Hz, and the relative error of component amplitude fluctuation is less than 1%. Root mean square error (RMSE) and 2-norm of estimated signal under different signal-to-noise ratio (SNR) are studied. When SNR is more than 12 dB, the error is within the acceptable range. RMSE and 2-norm tends to decline steadily when SNR is greater than 20 dB. By analyzing the simulation signals under different SNR, the correctness of the proposed algorithm is verified in this paper. It provides a new method for the mode decomposition of nonlinear chirp signal, which has a certain application value.
Keywords: nonlinear signal;mode decomposition;demodulation technology;wide-band signal;alternating direction method of multipliers
2020, 46(10):103-110  收稿日期: 2020-02-02;收到修改稿日期: 2020-04-02
基金项目: 国家重点研发计划项目(2017YFC0805005-1,2018YFC0810401);北京市教育委员会科研计划项目资助(KZ201810005009)
作者简介: 句海洋(1990-),男,河北衡水市人,博士研究生,研究方向为管道损伤地磁检测、信号与信息处理
参考文献
[1] 谭鑫. 隐伏断裂构造音频大地电磁法探测[J]. 工程地球物理学报, 2018, 15(3): 376-382
[2] 王超, 沈斐敏. 一维 HHT 变换在探地雷达数据处理中的应用[J]. 工程地质学报, 2015, 23(2): 328-334
[3] 李靖卿, 冯存前, 孙宏伟, 等. 基于混合体制雷达网的弹道目标微特征及外形参数提取[J]. 航空学报, 2016, 37(6): 1963-1973
[4] 管河山, 王谦, 唐德文. 基于分位数特征提取的时间序列模式分类[J]. 计算机工程, 2015, 41(3): 167-171
[5] 刘自然, 胡毅伟, 石璞, 等. 基于改进经验小波变换的滚动轴承故障特征提取方法研究[J]. 中国测试, 2019(10): 10-15
[6] COHEN F S, KADAMBE S, BOUDREAUX-BARTELS G F. Tracking of unknown nonstationary chirp signals using unsupervised clustering in the Wigner distribution space[J]. IEEE Transactions on Signal Processing, 1993, 41(11): 3085-3101
[7] HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings Mathematical Physical & Engineering Sciences, 1998, 454(1971): 903-995
[8] 陈凤林, 刘永斌, 方健, 等. 基于EMD与JADE的设备状态特征提取方法[J]. 计算机工程, 2015, 41(7): 305-309
[9] JAVED E, FAYE I, MALIK A S, et al. Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA[J]. Journal of Neuroscience Methods, 2017, 291: 150-165
[10] 贺静波, 彭复员. 基于改进 EMD 的图像压缩算法[J]. 红外与毫米波学报, 2008, 27(4): 295-298
[11] WU Z, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in adaptive data analysis, 2009, 1(1): 1-41
[12] YEH J R, SHIEH J S, HUANG N E. Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method[J]. Advances in Adaptive Data Analysis, 2010, 2(2): 135-156
[13] ZHOU F, YANG L, ZHOU H, et al. Optimal averages for nonlinear signal decompositions—Another alternative for empirical mode decomposition[J]. Signal Processing, 2016, 121: 17-29
[14] 郑近德, 程军圣, 曾鸣. 基于改进的局部特征尺度分解和归一化正交的时频分析方法[J]. 电子学报, 2015, 43(7): 1418-1424
[15] REHMAN N U, MANDIC D P. Filter bank property of multivariate empirical mode decomposition[J]. IEEE Transactions on Signal Processing, 2011, 59(5): 2421-2426
[16] RILLING G, FLANDRIN P. One or two frequencies? The empirical mode decomposition answers[J]. IEEE Transactions on Signal Processing, 2008, 56(1): 85-95
[17] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544
[18] GILLES J. Empirical wavelet transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16): 3999-4010
[19] BOYD S, PARIKH N, CHU E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations & Trends® in Machine Learning, 2010, 3(1): 1-122
[20] 张代雨. 多学科优化算法及其在水下航行器中的应用[D]. 西安: 西北工业大学, 2017.
[21] 张金刚, 相里斌, 汶德胜, 等. 采用色差先验约束的像差校正技术[J]. 中国光学, 2018(4): 560-567
[22] LIU T, LUO Z J, HUANG J H, et al. A comparative study of four kinds of adaptive decomposition algorithms and their applications[J]. Sensors, 2018, 18(7): 2120
[23] CHEN S, DONG X, XING G, et al. Separation of overlapped non-stationary signals by ridge path regrouping and intrinsic chirp component decomposition[J]. IEEE Sensors Journal, 2017, 17(18): 5994-6005