The analysis of ground penetrating radar signal based on generalized S transform with parameters optimization

Xue, Wei; Zhu, Jichao; Xia, Rong; Huang, Yulin; Yang, Yueting; Yu, Yunyun

doi:10.1016/j.jappgeo.2017.03.016

Cited by 23 publications

(11 citation statements)

References 15 publications

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“…The TFA methods have been successfully applied in a wide range of engineering applications such as mechanical, biomedical, seismic, speech, power, audio, and radar signal processing. [2][3][4][5][6][7] Linear TFA methods such as short-time Fourier transform (STFT) and continuous wavelet transform (WT) are most widely utilized to characterize nonstationary signals in the TF plane. 8 However, the effectiveness of the linear TFA methods is limited by the uncertainty principle, "which stipulates that one cannot localize a signal with arbitrary precision both in time and frequency."…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, many TFA methods have been developed to achieve high time and frequency resolution simultaneously. The TFA methods have been successfully applied in a wide range of engineering applications such as mechanical, biomedical, seismic, speech, power, audio, and radar signal processing 2‐7 . Linear TFA methods such as short‐time Fourier transform (STFT) and continuous wavelet transform (WT) are most widely utilized to characterize nonstationary signals in the TF plane 8 .…”

Section: Introductionmentioning

confidence: 99%

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Nonstationary waveform detection in power quality signals using multisynchrosqueezing transform

Sahu¹,

Dash

2021

Int Trans Electr Energ Syst

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Time-frequency (TF) analysis (TFA) is widely used as a reliable signalprocessing tool for the detection and identification of time-varying waveforms in power quality (PQ) signals. However, the effectiveness of a TFA method is limited by Heisenberg uncertainty principle. The presence of cross terms is also a significant issue in TF analysis of real-time signals. In PQ signal analysis, it is highly necessary to apply a suitable TFA technique with higher clarity and fewer cross-term problems. This paper introduces a newly developed TFA method known as multisynchrosqueezing transform (MSST) to detect the presence of nonstationary waveforms in PQ disturbance signals. The MSST is a high-resolution TFA method, which is similar to synchrosqueezing transform (SST) but has an iterative reassignment process to enhance the energy concentration of the TF representation. We apply the MSST to various types of single and combined PQ disturbance signals and show its potential application to PQ disturbance signal analysis. Results show that MSST provides higher TF resolution compared to other contemporary methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonstationary waveform detection in power quality signals using multisynchrosqueezing transform

Sahu¹,

Dash

2021

Int Trans Electr Energ Syst

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“…The energy (or magnitude) at a specific frequency can be viewed as a function of time. The spectral localisation properties of the ST has led to applications in many fields, such as biomedical engineering [10–16]; geophysics [17, 18]; biometric [19]; electrical engineering [20–26]; image processing [27, 28], etc. The ST has also been extended to design generalised ST [29], fractional ST [30, 31] and canonical ST [32].…”

Section: Introductionmentioning

confidence: 99%

Compact S‐transform for analysing local spectrum

Pradhan

Mansinha

2020

IET signal process.

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The Fourier transform of a N point time series is a N point complex series, while the S‐transform (ST) of the same time series is a N×N 2D time–frequency complex matrix. The computation and storage of N2−N additional points are a major drag on the usage of ST. In this study the compact S‐transform (cST) is presented, with efficiencies brought about through computation of only selected voices (frequencies). The cST spectrum has uncomputed voice gaps that increase in width towards the higher frequencies. Plot of the cST magnitude spectrum is virtually indistinguishable from the ST magnitude plot. Local spectrum at any spot on the cST can be quickly examined in detail through interpolation. The cST requires the computation of approximately 3N voices compared to falsefalse⌊N/2falsefalse⌋+1 for the ST. The proportion of computed voices decrease for larger N. For N=1024, ∼20% of the voices in the time‐frequency spectrum is computed; for N=2048 only 14% of the voices is computed. For applications, such as audio and speech signal processing where segments of one million samples are not uncommon, <1% of the voices are computed, thereby reducing the computation time by ∼99%.

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“…Since hydraulic engineering are often man-made, the size and internal material could be investigated through design data or other related data. GPR is a detection method [5] used for determining the internal distribution of a medium through high frequency electromagnetic waves and has an advantage in being able to detect these hidden defects, *For correspondence which has been widely applied in many fields [6][7][8][9][10][11][12][13]. Compared with other engineering structures, the complex hydrology, geology and climate environment involved with hydraulic structures greatly affect the GPR detection results, which makes it difficult to identify the signatures of structural defects based on radar images.…”

Section: Introductionmentioning

confidence: 99%

An experimental GPR detection study of environmentally-influenced structural defects in hydraulic engineering

et al. 2018

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Structural defects such as separation between concrete slab and foundation, and structural voids often occur in hydraulic engineering, which threatens the safety of hydraulic engineering. As the size and internal material of the hydraulic engineering can be queried, ground penetration radar (GPR) detection has the advantage to detect these defects when compared with other nondestructive detection methods. At the same time, when GPR detection is applied to defect these structural defects in hydraulic engineering, complex environmental factors including the uneven structural surface, clutter interference, water reflection, etc. have to be taken into account. In this work, two experimental models are designed to represent two different types of hydraulic structures and the structural defects including separation between concrete slab and its bottom material and void or hole defects are simulated on the two test models, respectively. Through the GPR detection on the two experimental models, the effects of the three environmental factors on the radar images and the signatures of the radar images under the influence of the three environmental factors are studied. Then, different image processing methods are adopted to reduce the influence of the three different environmental factors, and the effects of these methods are verified using the radar images obtained from the experiments. Finally, the GPR detection on a practical hydraulic engineering influenced by the environmental factors and the image processing methods are investigated, which successfully verify the experimental investigation results. It is expected that this study would provide significant technology support for structural defects detection in hydraulic engineering.

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The analysis of ground penetrating radar signal based on generalized S transform with parameters optimization

Cited by 23 publications

References 15 publications

Nonstationary waveform detection in power quality signals using multisynchrosqueezing transform

Nonstationary waveform detection in power quality signals using multisynchrosqueezing transform

Compact S‐transform for analysing local spectrum

An experimental GPR detection study of environmentally-influenced structural defects in hydraulic engineering

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