A nobel characterization of shape of pulse in GPR signal transmission

Kumar, Varun; Maiti, Subrata

doi:10.1109/iccsp.2014.6949983

Cited by 3 publications

(6 citation statements)

References 5 publications

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“…The shape of the time-domain signal is important for GPR ( 31 , 32 ). The waveform of the transmitted signal can affect the resistance of the signal to surrounding or internal noises.…”

Section: Methodsmentioning

confidence: 99%

“…Signals with zero phase in frequency domain are more resistant to phase distortion than non-zero-phase signals ( 33 , 34 ). A Ricker wavelet has a higher signal-to-noise ratio and spatial resolution as well as a lower power loss ( 31 ) than other wavelet shapes, such as Gaussian wavelet, Sine wavelet, and Raised cosine.…”

Section: Methodsmentioning

confidence: 99%

“…Pulse shaping is a signal-processing technique of changing the waveform of transmitted pulses to an ideal waveform ( 35 , 36 ). It is usually used for the optimality of radar system performance ( 31 , 37 ). Least-square adaptive filters, using gradient descent and least mean square error methods, respectively, were developed to reshape the original pulse waveform and to improve signal stability.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Signal Stability and the Height-Correction Method for Ground-Penetrating Radar In Situ Asphalt Concrete Density Prediction

Cao

Al‐Qadi

2021

Transportation Research Record

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Ground-penetrating radar (GPR) has shown great potential for asphalt concrete density prediction used in quality control and quality assurance. One challenge of continuous GPR measurements is that the measured dielectric constant could be affected by signal stability and antenna height. This would jeopardize the accuracy of the asphalt concrete density prediction along the pavement. In this study, signal instability and shifting antenna height during continuous real-time GPR measurements were identified as main sources of error. After using a bandpass filter to preprocess the signal, a least-square adaptive filter, using gradient descent and least mean square methods, was developed to reconstruct the received signal to improve its stability. In addition, simulations were performed to evaluate the impact of geometric spreading caused by shifting antenna height during testing. A height correction was developed using a power model to correct the height-change impact. The proposed filter and height-correction method were assessed using static and dynamic tests. The least-square adaptive filter improved signal stability by 50% and the height-correction method removed the effect of shifting antenna height almost entirely.

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“…The shape of the time-domain signal is important for GPR ( 31 , 32 ). The waveform of the transmitted signal can affect the resistance of the signal to surrounding or internal noises.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Signal Stability and the Height-Correction Method for Ground-Penetrating Radar In Situ Asphalt Concrete Density Prediction

Cao

Al‐Qadi

2021

Transportation Research Record

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“…where v is the speed of electromagnetic waves in the ground, B is the bandwidth and τ p is the pulse width [6]. Equation ( 3) and ( 4) imply that in order to gain large resolution it requires small bandwidth with a large pulse width.…”

Section: Pulse Shapingmentioning

confidence: 99%

“…In geo-radar, the method of generating seismic data with a pulse shape and a good signaling process requires a good method, one of the best is the Ricker wavelets [6,7]. Ricker wavelets is theoretically a solution to the Stokes differential equation that can be applied to propagated seismic waves [8].…”

Section: Introductionmentioning

confidence: 99%

The Effect Ricker Wavelet of Duty Cycle Adjustment on GPR Detection Result

Ramadhan

Ali²,

Pramudita³

2021

JMECS

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Ground Penetrating Radar (GPR) employs an ultra-wideband (UWB) signal for detecting objects under the ground surface. In a certain GPR application, a proper UWB signal is needed to obtain a good detection result. Ricker wavelet is one type of UWB signal that can be used in GPR operation. The effect of adjusting the Ricker wavelet duty cycle on the B-scan result was investigated and the result is discussed in this paper. Laboratory experiments were performed by modelling the GPR system using Vector Network Analyzer (VNA). The result shows that selecting a Ricker wavelet’s duty cycle is successful to show the target clearly.

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Ground Penetrating Radar in Coastal Hazard Mitigation Studies Using Deep Convolutional Neural Networks

2022

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There is a long history of coastal erosion caused by frequent storm surges in the coastal regions of Australia, which imposes great threats to communities and infrastructures alongside the beach. Old Bar Beach, New South Wales, Australia, is one such hotspot famous for its extreme coastal erosion. To apply remedial measures such as beach nourishment effectively and economically, estimating/reconstructing the subsurface hydrogeology over the coastal areas is essential. A geophysical tool such as a ground-penetrating radar (GPR) which works on the principle of reflecting electromagnetic (EM) waves, can be conveniently deployed to delineate the soil and rock profiling, water-table depth, bedrock depth, and the subsurface structural features. Here, DeepLabv3+ architecture based newly developed deep convolutional neural networks (DCNNs) were used to establish an inherent non-linear relationship between the GPR data and the EM wave velocity. The presented DCNNs have a lesser number of layers, a lesser number of trainable (learnable) parameters, a high convergence rate and, at the same time, achieve prediction accuracy comparable to that of well-established DeepLabv3+ networks, having high trainable parameters and a relatively low convergence rate. Here, firstly the DCNNs were trained and validated on small 1D datasets. Each dataset contains a 1D GPR trace and a corresponding EM velocity model. The DCNNs turned out to be quite promising in the 1D case, with training, validation, and testing accuracy of approximately 95%, 94%, and 95%, respectively. Secondly, 1D trained weights were applied to 2D synthetic GPR data for EM velocity prediction, and the accuracy of prediction achieved was approximately 95%. Seeing the excellent performance of the DCNNs in the 2D prediction case using 1D trained weights, a large amount of 1D synthetic datasets (approximately 1.2 million) were generated and gaussian noise was added to it to replicate the real field scenario. Thirdly, topographically corrected GPR data acquired over the Old Bar Beach were inverted using the DCNNs trained on 1.2 million 1D synthetic datasets to obtain the subsurface high-resolution, high-precision EM velocity, and εr distribution information to understand the hydrogeology over the beach. The findings presented in this paper agree well with the previous hydrogeological studies carried out using GPR. Our findings show that DCNNs, along with GPR, can be successfully used in coastal environments for the quick and accurate hydrogeological investigation required for the implementation of coastal erosion mitigation methods such as beach nourishment.

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A nobel characterization of shape of pulse in GPR signal transmission

Cited by 3 publications

References 5 publications

Signal Stability and the Height-Correction Method for Ground-Penetrating Radar In Situ Asphalt Concrete Density Prediction

Signal Stability and the Height-Correction Method for Ground-Penetrating Radar In Situ Asphalt Concrete Density Prediction

The Effect Ricker Wavelet of Duty Cycle Adjustment on GPR Detection Result

Ground Penetrating Radar in Coastal Hazard Mitigation Studies Using Deep Convolutional Neural Networks

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