An UWB Through-The-Wall radar with 3D imaging, detection and tracking capabilities

Millot, Patrick; Castanet, Laurent; Casadebaig, L.; Maaref, Nadia; Gaugue, Alain; Ménard, Michel; Khamlichi, Jamal; Louis, Georges; Fortino, Nicolas; Dauvignac, Jean-Yves; Clementi, G.; Schortgen, M.; Quellec, L.; Laroche, V.

doi:10.1109/eurad.2015.7346281

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…Pulse GPRs have a wide range of applications, e.g., UWB Through-The-Wall radar with 3D imaging is used as a through-the-wall radar demonstrator for identi cation purposes [11]; medium-frequency and lowfrequency GPRs are used for geological strati cation, and high-frequency radars, thanks to its high detection accuracy, allows distinguishing layers and objects at the centimeter level [12]; ultra-broadband radars with a metal detector sensor head have subcentimeter accuracy for detecting dangerous metal targets [13].…”

Section: Related Workmentioning

confidence: 99%

“…The proposed mine detection and recognition methods with UWB radars and arti cial neural networks [10][11][12][13][14][15][16][17][18][19][20] require further development of the landmine localization methodology for the demining task. The reason for that is a problem with the accuracy of determining the location of underground objects and registration of part of the re ected signal, which carries a piece of information about the underground object, that occurs when creating automated mine detection and recognition systems.…”

Section: Related Workmentioning

confidence: 99%

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Small military and industrial under-surface objects detection using data from the UWB GPR, MLP filter, and oscillatory neural network

Peleshchak

Lytvyn

Mediakov

et al. 2023

Preprint

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In the paper, we present a solution to the problem of recognizing subsurface objects detected by UWB radar using a hybrid neural network consisting of an MLP filter, a Hilbert block, and an oscillatory neural network. Based on information resonance, this hybrid neural network architecture recognizes low-amplitude noisy signals that are signs of subsurface objects. In addition, the proposed neural network model can filter reflected signals with interference and amplify valuable signals due to resonance and has higher performance than existing models of artificial neural networks.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Small military and industrial under-surface objects detection using data from the UWB GPR, MLP filter, and oscillatory neural network

Peleshchak

Lytvyn

Mediakov

et al. 2023

Preprint

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“…Pulse ground penetrating radars have a wide range of applications, including the UWB Through-The-Wall radar with 3D images used as a radar demonstrator «through the wall» in terms of identification [11]. Medium-frequency and low-frequency ground-based radars are used for geological stratification, and high-frequency ground-based radar, given its high detection accuracy, makes it possible to distinguish layers and objects at the centimeter level [12].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…4 shows the time dependence of the normalized amplitudes of the two reflected signals received by the UWB antenna system [18]. These signals are signs of PMN-1 and works [10][11][12][13][14][15][16][17][18][19][20] require further development of a methodology for localizing landmines for demining. Since the construction of automated systems for detecting and recognizing mines raises the problem of accuracy in determining the location of underground objects and registering part of the reflected signal, which is the carrier of information about the underground object.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of a hybrid neural network model for mine detection by using ultrawideband radar data

Lytvyn

Peleshchak

et al. 2023

EEJET

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The object of the study is the architecture of a hybrid neural network for mine recognition using ultra-wideband radar data. The work solves the problem of filtering reflected signals with interference and recognizing mines detected by ultra-wideband (UWB) radar. A hybrid neural network model in combination with the Adam learning algorithm is proposed. Filtering of reflected signals from mines is carried out using an MLP (multilayer perceptron) filter, which selects low-amplitude parts of signals that carry information about a hidden mine from the entire reflected signal. Mine recognition is carried out by a Hilbert block and an oscillatory neural network, which are included in the structure of a hybrid neural network. The peculiarity of the obtained results, which allowed to solve the investigated problem, is the transformation of the signal frequency by the Hilbert block and the recognition of mines by the oscillatory neural network in the resonant mode. The three-layer MLP filter effectively filters out the unwanted component in the total signal reflected from the subsurface object, as the MSE (Mean Squared Error) of the MLP filter is 1·10-5. If the frequency of the Hilbert signal is equal to the natural frequency of oscillations of neurons then the recognition of signals with a small amplitude from subsurface objects is carried out by an oscillatory neural network based on the resonant amplitude, which is indicated by a small value of cross-entropy. The proposed model of a hybrid neural network provides amplification of useful signals due to resonance and has higher performance compared to existing models of artificial neural networks. The practical significance of the obtained results lies in their application in the field of automated neural network technologies for detection and recognition of subsurface objects of various nature based on reflected radar signals with an amplitude at the noise level

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UWB Radar Technology for Imaging Applications

Koul

Bharadwaj

2021

Lecture Notes in Electrical Engineering

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An UWB Through-The-Wall radar with 3D imaging, detection and tracking capabilities

Cited by 9 publications

References 3 publications

Small military and industrial under-surface objects detection using data from the UWB GPR, MLP filter, and oscillatory neural network

Small military and industrial under-surface objects detection using data from the UWB GPR, MLP filter, and oscillatory neural network

Development of a hybrid neural network model for mine detection by using ultrawideband radar data

UWB Radar Technology for Imaging Applications

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