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Journal articles on the topic 'Synchrosqueezing transform'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Cheng, Xiaohan, Aiming Wang, Zongwu Li, Long Yuan, and Yajing Xiao. "An Enhanced Version of Second-Order Synchrosqueezing Transform Combined with Time-Frequency Image Texture Features to Detect Faults in Bearings." Shock and Vibration 2021 (April 26, 2021): 1–20. http://dx.doi.org/10.1155/2021/5589825.

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Signals with multiple components and fast-varying instantaneous frequencies reduce the readability of the time-frequency representations obtained by traditional synchrosqueezing transforms due to time-frequency blurring. We discussed a vertical synchrosqueezing transform, which is a second-order synchrosqueezing transform based on the short-time Fourier transform and compared it to the traditional short-time Fourier transform, synchrosqueezing transform, and another form of the second-order synchrosqueezing transform, the oblique synchrosqueezing transform. The quality of the time-frequency re

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HUANG, Zhonglai, and Jianzhong ZHANG. "Synchrosqueezing S-transform." SCIENTIA SINICA Informationis 46, no. 5 (2016): 643–50. http://dx.doi.org/10.1360/n112015-00011.

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3

Guan, Yunpeng, Ming Liang, and Dan-Sorin Necsulescu. "A velocity synchrosqueezing transform for fault diagnosis of planetary gearboxes under nonstationary conditions." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 15 (2016): 2868–84. http://dx.doi.org/10.1177/0954406216641455.

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Time–frequency analysis is widely used in the field of machinery condition monitoring and fault diagnosis under nonstationary conditions. Among the time–frequency methods synchrosqueezing transform outperforms others in providing fine-resolution time–frequency representation. However, it suffers from time–frequency smear when analysing nonstationary signals. To address this issue, this paper proposes a new synchrosqueezing-transform-based method which works by (1) mapping the raw nonstationary vibration signal into a corresponding stationary angle domain signal to meet the stationarity require

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4

Tu, Xiaotong, Yue Hu, Saqlain Abbas, and Fucai Li. "Generalized wavelet-based synchrosqueezing transform: Algorithm and applications." Structural Health Monitoring 19, no. 6 (2020): 2051–62. http://dx.doi.org/10.1177/1475921720906112.

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Time–frequency analysis is recognized as an efficient tool to characterize the time-varying feature from the oscillatory signal by transforming it into an identifiable form. Some traditional time–frequency transforms are subjected to poor time–frequency resolution or do not allow for mode reconstruction. As a postprocessing method, the synchrosqueezing transform has been utilized to tackle these problems. In this framework, a new method termed as generalized wavelet-based synchrosqueezing transform is developed in the current research work to deal with a strong modulated signal. The proposed m

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Colominas, Marcelo A., and Sylvain Meignen. "Adaptive order synchrosqueezing transform." Signal Processing 231 (June 2025): 109881. https://doi.org/10.1016/j.sigpro.2024.109881.

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Yu, Zhaohong, Cancan Yi, Xiangjun Chen, and Tao Huang. "Adaptive multiple second-order synchrosqueezing wavelet transform and its application in wind turbine gearbox fault diagnosis." Measurement Science and Technology 33, no. 1 (2021): 015110. http://dx.doi.org/10.1088/1361-6501/ac38ee.

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Abstract Wind turbines usually operate in harsh environments and in working conditions of variable speed, which easily causes their key components such as gearboxes to fail. The gearbox vibration signal of a wind turbine has nonstationary characteristics, and the existing time-frequency (TF) analysis (TFA) methods have some problems such as insufficient concentration of TF energy. In order to obtain a more apparent and more congregated time-frequency representation (TFR), this paper proposes a new TFA method, namely adaptive multiple second-order synchrosqueezing wavelet transform (AMWSST2). F

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Xiong, Hongqiang, Jing Li, Tengfei Zhang, Rong Hu, and Tieyu Liu. "Improved Synchrosqueezing S-Transform and Its Application in Reinforcement Protection Layer Identification." Journal of Physics: Conference Series 2887, no. 1 (2024): 012063. http://dx.doi.org/10.1088/1742-6596/2887/1/012063.

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Abstract Synchrosqueezing is a strategy to enhance the resolution of time-frequency analysis. However, its application in the S-transform leads to complexities due to the variable phase spectrum of the S-transform, resulting in a decrease in resolution and an increase in complexity. This work proposes an Synchrosqueezing Generalized S-Transform (SS-GPST) by introducing phase-shifting and a generalized parameter to simplify the mathematical definition and enhance resolution. Synthetic data trials demonstrate the superiority of SS-GPST over existing synchrosqueezing methods, offering enhanced re

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8

Ashtari Jafari, Mohammad. "Comparative Application of Time-Frequency Methods on Strong Motion Signals." Advances in Civil Engineering 2021 (July 31, 2021): 1–14. http://dx.doi.org/10.1155/2021/9933078.

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Real-world physical signals are commonly nonstationary, and their frequency details change with time and do not remain constant. Fourier transform that uses infinite sine/cosine waves as basis functions represents frequency constituents of signals but does not show the variations of the signal frequency contents over time. Multiresolution demonstration of the time-frequency domain may be achieved by the techniques that can support adjustable resolution in time and frequency. Earthquake strong motion signals are nonstationary and indicate time-varying frequency content due to the scattering fro

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9

Tu, Xiaotong, Zhoujie He, Yue Hu, Saqlain Abbas, and Fucai Li. "Horizontal Synchrosqueezing Transform: Algorithm and Applications." IEEE Sensors Journal 20, no. 8 (2020): 4353–60. http://dx.doi.org/10.1109/jsen.2020.2964109.

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Wei, Dahuan, Huang Huang, Zhengjie Lu, et al. "Iterative local maximum synchrosqueezing-extracting transform." Measurement 242 (January 2025): 115799. http://dx.doi.org/10.1016/j.measurement.2024.115799.

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11

Wang, Haibin, and Junbo Long. "Applications of Fractional Lower Order Synchrosqueezing Transform Time Frequency Technology to Machine Fault Diagnosis." Mathematical Problems in Engineering 2020 (August 3, 2020): 1–19. http://dx.doi.org/10.1155/2020/3983242.

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Synchrosqueezing transform (SST) is a high resolution time frequency representation technology for nonstationary signal analysis. The short time Fourier transform-based synchrosqueezing transform (FSST) and the S transform-based synchrosqueezing transform (SSST) time frequency methods are effective tools for bearing fault signal analysis. The fault signals belong to a non-Gaussian and nonstationary alpha (α) stable distribution with 1<α<2 and even the noises being also α stable distribution. The conventional FSST and SSST methods degenerate and even fail under α stable distribution noisy

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12

Xiong, Hongqiang, Baizhou An, Boyang Sun, and Jiayu Lu. "An Improved Synchrosqueezing S-Transform and Its Application in a GPR Detection Task." Sensors 24, no. 10 (2024): 2981. http://dx.doi.org/10.3390/s24102981.

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The S-transform is a fundamental time–frequency (T-F) domain analysis method in ground penetrating radar (GPR) data processing and can be used for identifying targets, denoising, extracting thin layers, and high-resolution imaging. However, the S-transform spectrum experiences energy leakage near the instantaneous frequency. This phenomenon causes frequency components to erroneously spread over a wider range, impacting the accuracy and precision of GPR data processing. Synchrosqueezing is an effective method to prevent spectrum leakage. In this work, we introduce the synchrosqueezing generaliz

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Zhang, Guowei, and Jinghuai Gao. "Inversion-Driven Attenuation Compensation Using Synchrosqueezing Transform." IEEE Geoscience and Remote Sensing Letters 15, no. 1 (2018): 132–36. http://dx.doi.org/10.1109/lgrs.2017.2777598.

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14

Cai, Haiyan, Qingtang Jiang, Lin Li, and Bruce W. Suter. "Analysis of adaptive short-time Fourier transform-based synchrosqueezing transform." Analysis and Applications 19, no. 01 (2020): 71–105. http://dx.doi.org/10.1142/s0219530520400047.

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Recently, the study of modeling a non-stationary signal as a superposition of amplitude and frequency-modulated Fourier-like oscillatory modes has been a very active research area. The synchrosqueezing transform (SST) is a powerful method for instantaneous frequency estimation and component separation of non-stationary multicomponent signals. The short-time Fourier transform-based SST (FSST) reassigns the frequency variable to sharpen the time-frequency representation and to separate the components of a multicomponent non-stationary signal. Very recently the FSST with a time-varying parameter,

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15

Xi, Song Tao, Hong Rui Cao, and Xue Feng Chen. "Zoom Synchrosqueezing Transform for Instantaneous Speed Estimation of High Speed Spindle." Materials Science Forum 836-837 (January 2016): 310–17. http://dx.doi.org/10.4028/www.scientific.net/msf.836-837.310.

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Instantaneous speed (IS) is of great significance of fault diagnosis and condition monitoring of the high speed spindle. In this paper, we propose a novel zoom synchrosqueezing transform (ZST) for IS estimation of the high speed spindle. Due to the limitation of the Heisenberg uncertainty principle, the conventional time-frequency analysis (TFA) methods cannot provide both good time and frequency resolution at the whole frequency region. Moreover, in most cases, the interested frequency component of a signal only locates in a narrow frequency region, so there is no need to analyze the signal i

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16

Wei, Dahuan, Kangguang Huang, Huang Huang, et al. "Local maximum multi-synchrosqueezing transform for the analysis of time-varying signals." Journal of Physics: Conference Series 2483, no. 1 (2023): 012025. http://dx.doi.org/10.1088/1742-6596/2483/1/012025.

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Abstract Synchrosqueezing transform (SST) and its improved algorithm are used to process time-varying signals and have been widely developed in the field of signal processing recently. However, processing strong time-varying signals is still a tricky problem. Multisynchrosqueezing transform (MSST) is an excellent time-frequency (TF) analysis technique for processing strong time-varying signals. However, some TF points will not be rearranged using this method. So we propose a new algorithm named local maximum multi-synchrosqueezing transform. The method detects the local maximum of MSST to addr

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17

Bing, Pingping, Wei Liu, Haoqi Zhang, et al. "Synchrosqueezing Transform Based on Frequency-Domain Gaussian-Modulated Linear Chirp Model for Seismic Time–Frequency Analysis." Mathematics 11, no. 13 (2023): 2904. http://dx.doi.org/10.3390/math11132904.

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The synchrosqueezing transform (SST) has attracted much attention as a post-processing technique since it was proposed. In recent years, improvements to SST have been made. However, the existing methods are mainly based on the time-domain signal model, and the weak frequency modulation assumption for the components composing the signal is always taken into account. Thus, the signals characterized by a rapidly changing instantaneous frequency (IF) may fail to be adequately tackled. To address this problem, the paper presents a novel seismic time–frequency analysis method via synchrosqueezing tr

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18

Zang, Bo, Mingzhe Zhu, Xianda Zhou, and Lu Zhong. "Application of S-Transform in ISAR Imaging." Electronics 8, no. 6 (2019): 676. http://dx.doi.org/10.3390/electronics8060676.

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In inverse synthetic aperture radar (ISAR) imaging, time-frequency analysis is the basic method for processing echo signals, which are reflected by the results of time-frequency analysis as each component changes over time. In the time-frequency map, a target’s rigid body components will appear as a series of single-frequency signals in the low-frequency region, and the micro-Doppler components generated by the target’s moving parts will be distributed in the high-frequency region with obvious frequency modulation. Among various time-frequency analysis methods, S-transform is especially suitab

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19

Hou, Yating, Liming Wang, Xiuli Luo, and Xingcheng Han. "Local maximum synchrosqueezes form scaling-basis chirplet transform." PLOS ONE 17, no. 11 (2022): e0278223. http://dx.doi.org/10.1371/journal.pone.0278223.

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In recent years, time-frequency analysis (TFA) methods have received widespread attention and undergone rapid development. However, traditional TFA methods cannot achieve the desired effect when dealing with nonstationary signals. Therefore, this study proposes a new TFA method called the local maximum synchrosqueezing scaling-basis chirplet transform (LMSBCT), which is a further improvement of the scaling-basis chirplet transform (SBCT) with energy rearrangement in frequency and can be viewed as a good combination of SBCT and local maximum synchrosqueezing transform. A better concentration in

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20

Baldini, Gianmarco, Raimondo Giuliani, and Gary Steri. "Physical Layer Authentication and Identification of Wireless Devices Using the Synchrosqueezing Transform." Applied Sciences 8, no. 11 (2018): 2167. http://dx.doi.org/10.3390/app8112167.

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This paper addresses the problem of authentication and identification of wireless devices using their physical properties derived from their Radio Frequency (RF) emissions. This technique is based on the concept that small differences in the physical implementation of wireless devices are significant enough and they are carried over to the RF emissions to distinguish wireless devices with high accuracy. The technique can be used both to authenticate the claimed identity of a wireless device or to identify one wireless device among others. In the literature, this technique has been implemented

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21

Qin, Xuan, Zhichao Yu, and Xueyan Yang. "A De-Noising Method for Microseismic Signal Based on the Synchrosqueezing Transform in the Time-Frequency Domain." Journal of Nanoelectronics and Optoelectronics 18, no. 3 (2023): 311–17. http://dx.doi.org/10.1166/jno.2023.3395.

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The denoising for the microseismic signals in the strong background interferences during the microseismic monitoring of hydraulic fracturing is very important for events identification and first-arrival picking. As characteristics of microseismic signals are normally random, non-stationary, and time-varying, a powerful tool for weak signal extraction of microseismic data is still a great challenge. In this study, a denoising method for microseismic signals is proposed based on the synchrosqueezing transform in the time-frequency domain. Firstly, the microseismic signals are denoised by the ada

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22

Li, Zhen, Jinghuai Gao, Hui Li, Zhuosheng Zhang, Naihao Liu, and Xiangxiang Zhu. "Synchroextracting transform: The theory analysis and comparisons with the synchrosqueezing transform." Signal Processing 166 (January 2020): 107243. http://dx.doi.org/10.1016/j.sigpro.2019.107243.

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23

Lou, Zhaoyu, Pan Li, Kang Ma, and Fengcheng Teng. "Harmonics and Interharmonics Detection Based on Synchrosqueezing Adaptive S-Transform." Energies 15, no. 13 (2022): 4539. http://dx.doi.org/10.3390/en15134539.

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The integration of renewable energy generation and nonlinear power electronic equipment into the grid brings about complex harmonics and interharmonics problems. The amplitude and frequency of harmonics and interharmonics should be detected by high time-frequency (T-F) resolution methods owing to their time-varying transient features. In this paper, a synchrosqueezing adaptive S-transform (SAST) method is proposed to detect the parameters of harmonics. Firstly, the time-frequency spectrum (TFS) of the harmonic signals is acquired by an adaptive S-transform (AST) algorithm. The TFS results are

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24

Cui, Zhenghao, Gang Yu, and Wei Tian. "S-Transform based time–frequency analysis tool with application to detection of bearing fault." Journal of Physics: Conference Series 2352, no. 1 (2022): 012004. http://dx.doi.org/10.1088/1742-6596/2352/1/012004.

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Faults developed in bearings are one of the prime reasons leading to mechanical damage. Recently, the novel technology synchrosqueezing extracting transform was proposed to capture the transient impulses in the non-stationary signal for fault diagnosis. Compared with traditional technology, this method has better anti-interference ability and faster processing speed. However, the method is a method based on short-time Fourier transform (STFT), S-transform (ST) is superior due to its higher time resolution in T-F spectrogram, ability to realize lossless inverse transformation, and property of r

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Zhu, Mingzhe, Zhenpeng Feng, Xianda Zhou, Rui Xiao, Yue Qi, and Xinliang Zhang. "Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar." Electronics 9, no. 4 (2020): 658. http://dx.doi.org/10.3390/electronics9040658.

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Time-frequency (TF) signal features are widely used in specific emitter identification (SEI) which commonly arises in many applications, especially for radar signals. Due to data scale and algorithm complexity, it is difficult to obtain an informative representation for SEI with existing TF features. In this paper, a feature extraction method is proposed based on synchrosqueezing transform (SST). The SST feature has an equivalent dimension to Fourier transform, and retains the most relevant information of the signal, leading to on average approximately 20 percent improvement in SEI for complex

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Jiang, Qingtang, and Bruce W. Suter. "Instantaneous frequency estimation based on synchrosqueezing wavelet transform." Signal Processing 138 (September 2017): 167–81. http://dx.doi.org/10.1016/j.sigpro.2017.03.007.

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Behera, Ratikanta, Sylvain Meignen, and Thomas Oberlin. "Theoretical analysis of the second-order synchrosqueezing transform." Applied and Computational Harmonic Analysis 45, no. 2 (2018): 379–404. http://dx.doi.org/10.1016/j.acha.2016.11.001.

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Marchi, Edgardo, Marcos Cervetto, and Cecilia Galarza. "Adaptive synchrosqueezing wavelet transform for real-time applications." Digital Signal Processing 140 (August 2023): 104133. http://dx.doi.org/10.1016/j.dsp.2023.104133.

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Chen, Tao, Qian Zheng, Lei Xie, and Hongye Su. "Sinusoidal-assisted synchrosqueezing transform: Algorithms and biomedical applications." Biomedical Signal Processing and Control 85 (August 2023): 105043. http://dx.doi.org/10.1016/j.bspc.2023.105043.

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30

Hemakom, Apit, Katarzyna Powezka, Valentin Goverdovsky, Usman Jaffer, and Danilo P. Mandic. "Quantifying team cooperation through intrinsic multi-scale measures: respiratory and cardiac synchronization in choir singers and surgical teams." Royal Society Open Science 4, no. 12 (2017): 170853. http://dx.doi.org/10.1098/rsos.170853.

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A highly localized data-association measure, termed intrinsic synchrosqueezing transform (ISC), is proposed for the analysis of coupled nonlinear and non-stationary multivariate signals. This is achieved based on a combination of noise-assisted multivariate empirical mode decomposition and short-time Fourier transform-based univariate and multivariate synchrosqueezing transforms. It is shown that the ISC outperforms six other combinations of algorithms in estimating degrees of synchrony in synthetic linear and nonlinear bivariate signals. Its advantage is further illustrated in the precise ide

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31

Tary, Jean Baptiste, Christine Peirce, and Richard W. Hobbs. "Classification of Bryde's whale individuals using high-resolution time-frequency transforms and support vector machines." Journal of the Acoustical Society of America 157, no. 3 (2025): 2091–101. https://doi.org/10.1121/10.0036223.

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Whales generate vocalizations which may, deliberately or not, encode caller identity cues. In this study, we analyze calls produced by Bryde's whales and recorded by ocean-bottom arrays of hydrophones deployed close to the Costa Rica Rift in the Panama Basin. These repetitive calls, consisting of two main frequency components at ∼20 and ∼36 Hz, have been shown to follow five coherent spatiotemporal tracks. Here, we use a high-resolution time-frequency transform, the fourth-order Fourier synchrosqueezing transform, to extract time-frequency characteristics (ridges) from each call to appraise th

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Tu, Xiaotong, Wenjie Bao, Yue Hu, Saqlain Abbas, and Fucai Li. "Parameterized Synchrosqueezing Transform With Application to Machine Fault Diagnosis." IEEE Sensors Journal 19, no. 18 (2019): 8107–15. http://dx.doi.org/10.1109/jsen.2019.2919776.

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Ping Wang, Jinghuai Gao, and Zhiguo Wang. "Time-Frequency Analysis of Seismic Data Using Synchrosqueezing Transform." IEEE Geoscience and Remote Sensing Letters 11, no. 12 (2014): 2042–44. http://dx.doi.org/10.1109/lgrs.2014.2317578.

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34

Wu, Guoning, and Yatong Zhou. "Seismic data analysis using synchrosqueezing short time Fourier transform." Journal of Geophysics and Engineering 15, no. 4 (2018): 1663–72. http://dx.doi.org/10.1088/1742-2140/aabf1d.

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Kumar, M. Suresh, G. Krishnamoorthy, and D. Vaithiyanathan. "Synchrosqueezing Transform Based Powerline Interference Reduction in ECG Recording." Journal of Medical Imaging and Health Informatics 10, no. 10 (2020): 2259–73. http://dx.doi.org/10.1166/jmihi.2020.3174.

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This paper presents an adaptive ECG enhancement procedure based on Synchrosqueezing Transform (SST) to eliminate Powerline interference (PLI) from ECG signal. This work also incorporates the principles of modified discrete cosine transform (MDCT) and wiener filter. PLI is a major source of artifacts in the ECG signal which can affect its interpretation. Separating PLI from ECG signal poses a great challenge in the ECG analysis. The existing PLI removal techniques suffer from two major drawbacks such as Mode Mixing, inability to deal with non-stationary characteristics of signal. In this paper,

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Cao, Hongrui, Songtao Xi, Xuefeng Chen, and Shibin Wang. "Zoom synchrosqueezing transform and iterative demodulation: Methods with application." Mechanical Systems and Signal Processing 72-73 (May 2016): 695–711. http://dx.doi.org/10.1016/j.ymssp.2015.11.030.

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Wang, Shibin, Xuefeng Chen, Gaigai Cai, Binqiang Chen, Xiang Li, and Zhengjia He. "Matching Demodulation Transform and SynchroSqueezing in Time-Frequency Analysis." IEEE Transactions on Signal Processing 62, no. 1 (2014): 69–84. http://dx.doi.org/10.1109/tsp.2013.2276393.

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Liu, Jing-Liang, Zuo-Cai Wang, Wei-Xin Ren, and Xing-Xin Li. "Structural time-varying damage detection using synchrosqueezing wavelet transform." Smart Structures and Systems 15, no. 1 (2015): 119–33. http://dx.doi.org/10.12989/sss.2015.15.1.119.

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Tary, Jean Baptiste, Roberto Henry Herrera, and Mirko van der Baan. "Analysis of time-varying signals using continuous wavelet and synchrosqueezed transforms." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376, no. 2126 (2018): 20170254. http://dx.doi.org/10.1098/rsta.2017.0254.

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The continuous wavelet transform (CWT) has played a key role in the analysis of time-frequency information in many different fields of science and engineering. It builds on the classical short-time Fourier transform but allows for variable time-frequency resolution. Yet, interpretation of the resulting spectral decomposition is often hindered by smearing and leakage of individual frequency components. Computation of instantaneous frequencies, combined by frequency reassignment, may then be applied by highly localized techniques, such as the synchrosqueezing transform and ConceFT, in order to r

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Dong, Haoran, Gang Yu, and Yueyang Li. "Theoretical analysis and comparison of transient-extracting transform and time-reassigned synchrosqueezing transform." Mechanical Systems and Signal Processing 178 (October 2022): 109190. http://dx.doi.org/10.1016/j.ymssp.2022.109190.

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Dong, Haoran, and Gang Yu. "Comments on “Synchroextracting transform: The theory analysis and comparisons with the synchrosqueezing transform”." Signal Processing 190 (January 2022): 108345. http://dx.doi.org/10.1016/j.sigpro.2021.108345.

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Li, Lin, Haiyan Cai, Hongxia Han, Qingtang Jiang, and Hongbing Ji. "Adaptive short-time Fourier transform and synchrosqueezing transform for non-stationary signal separation." Signal Processing 166 (January 2020): 107231. http://dx.doi.org/10.1016/j.sigpro.2019.07.024.

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Wu, Guoning, Guochang Liu, Junxian Wang, and Pingping Fan. "Seismic Random Noise Denoising Using Mini-Batch Multivariate Variational Mode Decomposition." Computational Intelligence and Neuroscience 2022 (February 26, 2022): 1–14. http://dx.doi.org/10.1155/2022/2132732.

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Seismic noise attenuation plays an important role in seismic interpretation. The empirical mode decomposition, synchrosqueezing wavelet transform, variational mode decomposition, etc., are often applied trace by trace. Multivariate empirical mode decomposition, multivariate synchrosqueezing wavelet transform, and multivariate variational mode decomposition were proposed for lateral continuity consideration. Due to large input data, mini-batch multivariate variational mode decomposition is proposed in this paper. The proposed method takes advantages both of variational mode decomposition and mu

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Herrera, Roberto H., Jiajun Han, and Mirko van der Baan. "Applications of the synchrosqueezing transform in seismic time-frequency analysis." GEOPHYSICS 79, no. 3 (2014): V55—V64. http://dx.doi.org/10.1190/geo2013-0204.1.

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Time-frequency representation of seismic signals provides a source of information that is usually hidden in the Fourier spectrum. The short-time Fourier transform and the wavelet transform are the principal approaches to simultaneously decompose a signal into time and frequency components. Known limitations, such as trade-offs between time and frequency resolution, may be overcome by alternative techniques that extract instantaneous modal components. Empirical mode decomposition aims to decompose a signal into components that are well separated in the time-frequency plane allowing the reconstr

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Zhang, Peng, Hongyuan Wen, Zhiyong Xu, and Zhao Zhao. "Improving the Robustness of Time Difference of Arrival Estimation Based on the Energy Center of Gravity Rearrangement." Sensors 23, no. 24 (2023): 9720. http://dx.doi.org/10.3390/s23249720.

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An accurate estimation of the time difference of arrival (TDOA) is crucial in localization, communication, and navigation. However, a low signal-to-noise ratio (SNR) can decrease the reliability of the TDOA estimation result. Therefore, this study aims to improve the performance of the TDOA estimation of dual-channel sensors for single-sound sources in low-SNR environments. This study introduces the theory of time rearrangement synchrosqueezing transform (TRST) into the time difference of arrival estimation. While the background noise TF points show random time delays, the signal time-frequenc

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WU, HAU-TIENG, PATRICK FLANDRIN, and INGRID DAUBECHIES. "ONE OR TWO FREQUENCIES? THE SYNCHROSQUEEZING ANSWERS." Advances in Adaptive Data Analysis 03, no. 01n02 (2011): 29–39. http://dx.doi.org/10.1142/s179353691100074x.

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The synchrosqueezed transform was proposed recently in [Daubechies et al. (2009)] as an alternative to the empirical mode decomposition (EMD) [Huang et al. (1998)], to decompose composite signals into a sum of "modes" that each have well-defined instantaneous frequencies. This paper presents, for synchrosqueezing, a study similar to that in [Rilling and Flandrin (2008)] for EMD, of how two signals with close frequencies are recognized and represented as such.

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Yuan, Jing, Ze Yao, Qian Zhao, Yiyu Xu, Chao Li, and Huiming Jiang. "Dual-Core Denoised Synchrosqueezing Wavelet Transform for Gear Fault Detection." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–11. http://dx.doi.org/10.1109/tim.2021.3094838.

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Wang, Xiaokai, Chun Li, and Wenchao Chen. "Seismic Thin Interbeds Analysis Based on High-Order Synchrosqueezing Transform." IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1–11. http://dx.doi.org/10.1109/tgrs.2021.3129627.

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Fang, Yuxia, Ying Hu, Mengyuan Li, Hui Chen, Xuping Chen, and Jun Li. "Second-Order Horizontal Multi-Synchrosqueezing Transform for Hydrocarbon Reservoir Identification." IEEE Geoscience and Remote Sensing Letters 19 (2022): 1–5. http://dx.doi.org/10.1109/lgrs.2021.3124813.

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Peng, Chuang, Xiaojing Yang, and Yu Zhang. "Blind separation of asymmetric signals based on Synchrosqueezing Wavelet Transform." MATEC Web of Conferences 232 (2018): 01050. http://dx.doi.org/10.1051/matecconf/201823201050.

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A blind separation algorithm based on synchronous squeezing wavelet transform is proposed to solve the blind separation problem of single channel asymmetric signals in satellite communications. First, the algorithm is used to synchronize the strong signal. Then, the signal time-frequency curve is extracted by synchronous extrusion wavelet transform. Finally, the weak signal interference is filtered out from the mixed signal except the noise and the main frequency of the strong signal. Therefore, the ber of the strong signal demodulation is reduced. The algorithm has the characteristics of blin

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