Dynamic mode decomposition-based technique for cross-term suppression in the Wigner-Ville distribution

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Digital Signal Processing Pub Date : 2024-10-29 DOI:10.1016/j.dsp.2024.104833
Alavala Siva Sankar Reddy, Ram Bilas Pachori
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Abstract

This paper presents a new method for time-frequency representation (TFR) using dynamic mode decomposition (DMD) and Wigner-Ville distribution (WVD), which is termed as DMD-WVD. The proposed method helps in removing cross-term in WVD-based TFR. In the suggested method, the DMD decomposes the multi-component signal into a set of modes where each mode is considered as mono-component signal. The analytic modes of these obtained mono-component signals are computed using the Hilbert transform. The WVD is computed for each analytic mode and added together to obtain cross-term free TFR based on the WVD. The effectiveness of the proposed method for TFR is evaluated using Rényi entropy (RE). Experimental results for synthetic signals namely, multi-component amplitude modulated signal, multi-component linear frequency modulated (LFM) signal, multi-component nonlinear frequency modulated (NLFM) signal, multi-component signal consisting of LFM and NLFM mono-component signal, multi-component signal consisting of sinusoidal and quadratic frequency modulated mono-component signals, and synthetic mechanical bearing fault signal and natural signals namely, electroencephalogram (EEG) and bat echolocation signals are presented in order to show the effectiveness of the proposed method for TFR. It is clear from the results that the proposed method suppresses cross-term effectively as compared to the other existing methods namely, smoothed pseudo WVD (SPWVD), empirical mode decomposition (EMD)-WVD, EMD-SPWVD, variational mode decomposition (VMD)-WVD, VMD-SPWVD, and DMD-SPWVD.
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基于动态模式分解的维格纳-维尔分布交叉项抑制技术
本文提出了一种使用动态模式分解(DMD)和维格纳-维尔分布(WVD)进行时频表示(TFR)的新方法,称为 DMD-WVD。建议的方法有助于消除基于 WVD 的 TFR 中的交叉项。在建议的方法中,DMD 将多分量信号分解为一组模式,其中每个模式都被视为单分量信号。利用希尔伯特变换计算这些单分量信号的解析模式。计算每个解析模式的 WVD 值,并将其相加,以获得基于 WVD 值的无跨期 TFR。利用雷尼熵 (RE) 评估了所提出的 TFR 方法的有效性。实验结果包括合成信号(即多分量幅度调制信号、多分量线性频率调制(LFM)信号、多分量非线性频率调制(NLFM)信号、由 LFM 和 NLFM 单分量信号组成的多分量信号、由正弦和二次频率调制单分量信号组成的多分量信号)、合成机械轴承故障信号以及自然信号(即脑电图(EEG)和蝙蝠回声定位信号),以显示所提方法对 TFR 的有效性。结果表明,与其他现有方法(即平滑伪 WVD(SPWVD)、经验模式分解(EMD)-WVD、EMD-SPWVD、变异模式分解(VMD)-WVD、VMD-SPWVD 和 DMD-SPWVD)相比,拟议方法能有效抑制交叉项。
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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