基于正则化线性调频模式追踪算法的信号消噪方法及其应用

上官甲新; 张淑清; 郑龙江; 姜安琦; 艾洪克; 张立国; 刘海涛

doi:10.3969/j.issn.1000-1158.2022.06.14

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计量学报 ›› 2022, Vol. 43 ›› Issue (6) : 798-804. DOI: 10.3969/j.issn.1000-1158.2022.06.14

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基于正则化线性调频模式追踪算法的信号消噪方法及其应用

上官甲新¹,张淑清¹,郑龙江¹,姜安琦¹,艾洪克²,张立国¹,刘海涛¹

作者信息 +

Signal Denoising Method Based on Regularized Chirp Mode Pursuit Algorithm and Its Applications

SHANGGUAN Jia-xin¹,ZHANG Shu-qing¹,ZHENG Long-jiang¹,Jiang An-qi¹,AI Hong-ke²,ZHANG Li-guo¹,LIU Hai-tao¹

Author information +

文章历史 +

摘要

信号在采集过程中存在的噪声会给后续工作造成一定程度的困难,因此消除噪声干扰是准确进行下一步工作的关键问题。提出了一种正则化线性调频模式追踪(regularized chirp mode pursuit, RCMP)算法,该算法通过先验频率信息及递归框架提取信号中的每一个信号模式,具有一定的自适应性。将提出的RCMP算法应用到传感器数据降噪中,通过仿真信号和实际信号研究了RCMP算法的去噪性能。同时,通过与EMD结合区间阈值的降噪方法、CEEMDAN结合区间阈值的降噪方法以及VMD结合相关系数的降噪方法进行对比,验证了所提方法的可行性和优越性。

Abstract

The noise in the signal acquisition process will cause a certain degree of difficulty to the follow-up work, so the elimination of noise interference is the key problem for the accurate next work. A regularized chirp mode pursuit (RCMP) algorithm is proposed. The algorithm extracts each chirp mode from the signal through a priori frequency information and a recursive framework, which has a certain degree of adaptability. The proposed RCMP algorithm is applied to the noise reduction of sensor data, and the noise reduction performance of the RCMP algorithm is studied through simulatd signals and actual signals. At the same time, the feasibility and superiority of the proposed method are verified by comparing the noise reduction method with EMD combined with interval threshold, CEEMDAN combined with interval threshold and VMD combined with correlation coefficient.

导出引用

上官甲新,张淑清,郑龙江,姜安琦,艾洪克,张立国,刘海涛. 基于正则化线性调频模式追踪算法的信号消噪方法及其应用[J]. 计量学报. 2022, 43(6): 798-804 https://doi.org/10.3969/j.issn.1000-1158.2022.06.14

SHANGGUAN Jia-xin,ZHANG Shu-qing,ZHENG Long-jiang,Jiang An-qi,AI Hong-ke,ZHANG Li-guo,LIU Hai-tao. Signal Denoising Method Based on Regularized Chirp Mode Pursuit Algorithm and Its Applications[J]. Acta Metrologica Sinica. 2022, 43(6): 798-804 https://doi.org/10.3969/j.issn.1000-1158.2022.06.14

中图分类号： TB973

参考文献

［1］郑海明, 刘佳. 差分吸收光谱测量臭氧浓度信号去噪的实验研究［J］. 计量学报, 2020, 41(6): 759-764.
Zheng H M, Liu J. Experimental Study on Denoising of Ozone Concentration Signal by Differential Absorption Spectroscopy ［J］. Acta Metrologica Sinica, 2020, 41(6): 759-764.
［2］Xu X B, Luo M Z, Tan Z Y, et al. Echo signal extraction method of laser radar based on improved singular value decomposition and wavelet threshold denoising ［J］. Infrared Physics and Technology, 2018, 92: 327-335.
［3］马敏, 王涛. 双小波非凸稀疏正则化去噪算法研究［J］. 计量学报, 2021, 42(1): 85-90.
Ma M, Wang T. Research on Double Wavelet Nonconvex Sparse Regularization Denoising Algorithm ［J］. Acta Metrologica Sinica, 2021, 42(1): 85-90.
［4］Chang J H, Zhu L Y, Li H X, et al. Noise reduction in Lidar signal using correlation-based EMD combined with soft thresholding and roughness penalty ［J］. Optics Communications, 2018, 407: 290-295.
［5］陈真诚, 吴贤亮, 赵飞骏. EEMD结合小波阈值的光电容积脉搏波信号降噪［J］. 光学精密工程, 2019, 27(6): 1327-1334.
Chen Z C, Wu X L, Zhao F J. Denoising and implementation of Photoplethysmography signal based on EEMD and wavelet threshold ［J］. Optics and Precision Engineering, 2019, 27(6): 1327-1334.
［6］贾亚超, 李国龙, 何坤, 等. 基于灰色准则与EEMD的滚刀振动信号降噪方法［J］. 仪器仪表学报, 2019, 40(7): 187-194.
Jia Y C, Li G L, He K, et al. Denoising method for vibration signal of hob based on grey criterion and EEMD ［J］. Chinese Journal of Scientific Instrument, 2019, 40(7): 187-194.
［7］樊凤杰, 白洋, 纪会芳. 基于EEMD-ICA的脑电去噪算法研究［J］. 计量学报, 2021, 42(3): 395-400.
Fan F J, Bai Y, Ji H F. Denoising Method of EEG Signal Based on EEMD-ICA ［J］. Acta Metrologica Sinica, 2021, 42(3): 395-400.
［8］徐利, 徐久强, 冯家乐. 结合CEEMDAN与改进区间阈值的ECG降噪研究［J］. 小型微型计算机系统, 2020, 41(8): 1576-1579.
Xu L, Xu J Q, Feng J L. Research on ECG Denoising Combined with CEEMDAN and Improved Interval Threshold ［J］. Journal of Chinese Computer Systems, 2020, 41(8): 1576-1579.
［9］Zhang S Q, Liu H T, Hu M F, et al. An Adaptive CEEMDAN Thresholding Denoising Method Optimized by Nonlocal Means Algorithm ［J］. IEEE Transactions on Instrumentation and Measurement, 2020, 69(9): 6891-6903.
［10］陆振宇, 卢亚敏, 夏志巍, 等. 基于变分模态分解和小波分析的语音信号去噪方法［J］. 现代电子技术, 2018, 41(13): 47-51.
Lu Z Y, Lu Y M, Xia Z W, et al. Speech signal denoising method based on VMD and wavelet analysis ［J］. Modern Electronics Technique, 2018, 41(13): 47-51.
［11］陈召全, 朱明星, 章小兵, 等. 基于模糊控制的小波包多阈值语音减噪新算法［J］. 计量学报, 2019, 40(1): 134-139.
Chen Z Q, Zhu M X, Zhang X B, et al. A New Algorithm of Wavelet Packet Multi Threshold Speech Denoising Based on Fuzzy Control ［J］. Acta Metrologica Sinica, 2019, 40(1): 134-139.
［12］徐妍琰, 孟建军, 张宇彤, 等. 基于小波分析的脉搏采集信号处理研究［J］. 工业控制计算机, 2020, 33(12): 63-64+67.
Xu Y T, Meng J J, Zhang Y T, et al. Research on Pulse Acquisition Signal Processing Based on Wavelet Analysis ［J］. Industrial Control Computer, 2020, 33(12): 63-64+67.
［13］Bayer F M, Kozakevicius A J, Cintra R J. An iterative wavelet threshold for signal denoising ［J］. Signal Processing, 2019, 162: 10-20.
［14］Zhang X, Li J L, Xing J C, et al. A Particle Swarm Optimization Technique-Based Parametric Wavelet Thresholding Function for Signal Denoising ［J］. Circuits, Systems, and Signal Processing, 2017, 36(1): 247-269.
［15］Kabir A, Shahnaz C. Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains ［J］. Biomedical Signal Processing and Control, 2012, 7(5): 481-489.
［16］Zhang S, Li Y. Seismic exploration desert noise suppression based on complete ensemble empirical mode decomposition with adaptive noise ［J］. Journal of Applied Geophysics, 2020, 180.
［17］Chen S Q, Yang Y, Peng Z K, et al. Adaptive chirp mode pursuit: Algorithm and applications ［J］. Mechanical Systems and Signal Processing, 2019, 116: 566-584.
［18］Chen S Q, Dong X J, Peng Z K, et al. Nonlinear Chirp Mode Decomposition: A Variational Method ［J］. IEEE Transactions on Signal Processing, 2017, 65(22): 6024-6037.
［19］Lv Q Y, Ye D X, Qiao S, et al. High dynamic-range motion imaging based on linearized doppler radar sensor ［J］. IEEE Transactions on Microwave Theory and Techniques, 2014, 62(9): 1837-1846.
［20］McNeill S I. Decomposing a signal into short-time narrow-banded modes ［J］. Journal of Sound and Vibration, 2016, 373: 325-339.
［21］Feldman M. Time-varying vibration decomposition and analysis based on the Hilbert transform ［J］. Journal of Sound and Vibration, 2006, 295(3-5): 518-530.