摘要翻译：
与传统的经验模态分解(EMD)方法相比，变分模态分解(VMD)具有较强的理论基础和较高的运算效率。将VMD方法引入探地雷达信号处理中。探地雷达信号的特点验证了基于VMD原理的信号去噪方法。通过Ricker小波和正演模型探地雷达去噪实验，进一步验证了该方法的有效性和准确性。通过与传统的小波变换(WT)和EEMD（集成EMD）方法的比较，对VMD方法进行了评价。然后用该方法对一个实际工程中的探地雷达信号进行了分析。结果表明，该方法能有效地去除探地雷达数据中的噪声，即使在强背景噪声下也能获得较高的信噪比。
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英文标题：
《GPR signal de-noise method based on variational mode decomposition》
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作者：
Juncai Xu, Zhenzhong Shen, Qingwen Ren, Xin Xie, and Zhengyu Yang
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最新提交年份：
2017
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Compared with traditional empirical mode decomposition (EMD) methods, variational mode decomposition (VMD) has strong theoretical foundation and high operational efficiency. The VMD method is introduced to ground penetrating radar (GPR) signal processing. The characteristics of GPR signals validate the method of signal de-noising based on the VMD principle. The validity and accuracy of the method are further verified via Ricker wavelet and forward model GPR de-noising experiments. The method of VMD is evaluated in comparison with traditional wavelet transform (WT) and EEMD (ensemble EMD) methods. The method is subsequently used to analyze a GPR signal from a practical engineering case. The results show that the method can effectively remove the noise in the GPR data, and can obtain high signal-to-noise ratios (SNR) even under strong background noise.
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PDF链接：
https://arxiv.org/pdf/1710.00779