Noise Reduction in a Non-Homogenous Ground Penetrating Radar Problem by Multiobjective Neural Networks

Travassos, Xisto Lucas; Vieira, D.A.G.; Palade, Vasile; Nicolas, A.

doi:10.1109/tmag.2009.2012677

Cited by 11 publications

(8 citation statements)

References 10 publications

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“…Notice that in this paper the noise is not considered. Here we just offer a principle of velocity measurement and the noise can be reduced by other methods such as the way in [16]. How to obtain the target velocity in engineering will be discussed later.…”

Section: Discussionmentioning

confidence: 99%

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A Velocity Measurement Method Based on Scaling Parameter Estimation of a Chaotic System

Liu¹,

Hu²,

He³

et al. 2011

Metrology and Measurement Systems

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A Velocity Measurement Method Based on Scaling Parameter Estimation of a Chaotic SystemIn this paper, we propose a new method of measuring the target velocity by estimating the scaling parameter of a chaos-generating system. First, we derive the relation between the target velocity and the scaling parameter of the chaos-generating system. Then a new method for scaling parameter estimation of the chaotic system is proposed by exploiting the chaotic synchronization property. Finally, numerical simulations show the effectiveness of the proposed method in target velocity measurement.

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Section: Discussionmentioning

confidence: 99%

“…Here the typical Lorenz chaotic system is used for illustration and it is shown in Eq. (16). The radar signal is generated by 1 x component in the Lorenz chaotic system.…”

Section: Numerical Simulationmentioning

confidence: 99%

A Velocity Measurement Method Based on Scaling Parameter Estimation of a Chaotic System

Liu¹,

Hu²,

He³

et al. 2011

Metrology and Measurement Systems

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show abstract

“…In general, a typical GPR procedure to detect underground targets can be summarized as: the received time waveform can be described as the convolution of a number of time functions each representing the impulse response of some component of the radar system in addition to noise contributions [5,41]. It is possible to simplify a GPR received time waveform as follows:…”

Section: Generic Implementationmentioning

confidence: 99%

“…[40] Frequency response function (FRF) and ANN. [41] Multiobjective neural networks (MNNs). [42] ANNs and curve fitting techniques.…”

Section: Background: Ann and ML For Gprmentioning

confidence: 99%

Artificial Neural Networks and Machine Learning techniques applied to Ground Penetrating Radar: A review

Travassos

Avila

Ida

2020

ACI

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Ground Penetrating Radar is a multidisciplinary Nondestructive Evaluation technique that requires knowledge of electromagnetic wave propagation, material properties and antenna theory. Under some circumstances this tool may require auxiliary algorithms to improve the interpretation of the collected data. Detection, location and definition of target’s geometrical and physical properties with a low false alarm rate are the objectives of these signal post-processing methods. Basic approaches are focused in the first two objectives while more robust and complex techniques deal with all objectives at once. This work reviews the use of Artificial Neural Networks and Machine Learning for data interpretation of Ground Penetrating Radar surveys. We show that these computational techniques have progressed GPR forward from locating and testing to imaging and diagnosis approaches.

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“…In [26], the authors propose to enhance the GPR signal with the Karhunen-Loève transform (KLT), whereas the work in [27] aims at improving the SNR of a GPR signal by introducing an enhanced-signal-based method, with the noise variance being estimated by a clustering technique. Furthermore, a novel pre-processing method for GPR signals, based on the minimum gradient method, is discussed in [28]. Within the most established signal processing techniques performed in the GPR area we can cite time and frequency analyses [29], time varying band-pass filtering [30], deconvolution [31], velocity analysis [32], migration [33] and compressive sensing [34], as well as the attribute analysis and classification [35].…”

Section: Still At the European Level A Number Of Standards And Codesmentioning

confidence: 99%

A signal processing methodology for assessing the performance of ASTM standard test methods for GPR systems

Benedetto

Tosti

2017

Signal Processing

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Ground penetrating radar (GPR) is one of the most promising and effective non-destructive testing techniques (NDTs), particularly for the interpretation of the soil properties. Within the framework of international Agencies dealing with the standardization of NDTs, the American Society for Testing and Materials (ASTM) has published several standard test methods related to GPR, none of which is focused on a detailed analysis of the system performance, particularly in terms of precision and bias of the testing variable under consideration. This work proposes a GPR signal processing methodology, calibrated and validated on the basis of a consistent amount of data collected by means of laboratory-scale tests, to assess the performance of the above standard test methods for GPR systems. The (theoretical) expressions of the bias and variance of the estimation error are here investigated by a reduced Taylor's expansion up to the second order. Therefore, a closed form expression for theoretically tuning the optimal threshold according to a fixed target value of the GPR signal stability is proposed. Finally, the study is extended to GPR systems with different antenna frequencies to analyze the specific relationship between the frequency of investigation, the optimal thresholds, and the signal stability

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Noise Reduction in a Non-Homogenous Ground Penetrating Radar Problem by Multiobjective Neural Networks

Cited by 11 publications

References 10 publications

A Velocity Measurement Method Based on Scaling Parameter Estimation of a Chaotic System

A Velocity Measurement Method Based on Scaling Parameter Estimation of a Chaotic System

Artificial Neural Networks and Machine Learning techniques applied to Ground Penetrating Radar: A review

A signal processing methodology for assessing the performance of ASTM standard test methods for GPR systems

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