UWB Technology for Detecting Alive People Behind Optically Opaque Obstacles

Sytnik, O. V.; Masalov, S. A.; Kholod, P. V.; Pochanin, Gennadiy; Ruban, V. P.

doi:10.1109/uwbusis.2018.8519970

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…One promising approach to this uses UWB GPR [7], which radiates ultrashort electromagnetic impulses to provide high spatial resolution of a survey region [8]. There is a wide range of applications for this technology, such as detecting humans, including people hidden behind opaque obstacles [9], soil analysis for subway construction [10], and the humanitarian demining activities mentioned above [11]. Mounting GPR systems on robotic platforms [2,12] and on drones [13] provides additional safety.…”

Section: Introductionmentioning

confidence: 99%

Implementation of an Artificial Intelligence Approach to GPR Systems for Landmine Detection

et al. 2022

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Artificial Neural Network (ANN) approaches are applied to detect and determine the object class using a special set of the UltraWideBand (UWB) pulse Ground Penetrating Radar (GPR) sounding results. It used the results of GPR sounding with the antenna system, consisting of one radiator and four receiving antennas located around the transmitting antenna. The presence of four receiving antennas and, accordingly, the signals received from four spatially separated positions of the antennas provide a collection of signals received after reflection from an object at different angles and, due to this, to determine the location of the object in a coordinate system, connected to the antenna. We considered the sums and differences of signals received by two of the four antennas in six possible combinations: (1 and 2, 1 and 3, 2 and 3, 1 and 4, etc.). These combinations were then stacked sequentially one by one into one long signal. Synthetic signals constructed in such a way contain many more notable differences and specific information about the class to which the object belongs as well as the location of the searched object compared to the signals obtained by an antenna system with just one radiating and one receiving antenna. It therefore increases the accuracy in determining the object’s coordinates and its classification. The pulse radiation, propagation, and scattering are numerically simulated by the finite difference time domain (FDTD) method. Results from the experiment on mine detection are used to examine ANN too. The set of signals from different objects having different distances from the GPR was used as a training and testing dataset for ANN. The training aims to recognize and classify the detected object as a landmine or other object and to determine its location. The influence of Gaussian noise added to the signals on noise immunity of ANN was investigated. The recognition results obtained by using an ANN ensemble are presented. The ensemble consists of fully connected and recurrent neural networks, gated recurrent units, and a long-short term memory network. The results of the recognition by all ANNs are processed by a meta network to provide a better quality of underground object classification.

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Section: Introductionmentioning

confidence: 99%

Implementation of an Artificial Intelligence Approach to GPR Systems for Landmine Detection

et al. 2022

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Signal Processing in UWB Subsurface Radiolocation by Artificial Neural Networks

Dumin

Plakhtii

Pryshchenko

et al. 2019

2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T)

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Comparison of subsurface object recognition by artificial neural networks and correlation method

Dumin¹,

Pryshchenko²,

Plakhtii³

et al. 2020

Visnyk of V.N. Karazin Kharkiv National University, Series “Radio Physics and Electronics”

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Background: The problem of searching for subsurface objects has a particular interest for construction, archeology and humanitarian demining. Detection of underground mines with the help of remote sensing devices replaces the traditional procedure of finding explosive objects, as it excludes the presence of a human in the area of possible damage during a charge explosion. Objectives: The aim of the work is to improve the recognition of three-dimensional objects and demonstrate the benefits of using a more informative data set obtained by a special antenna system with four receiving antennas. In addition, it is necessary to compare the effectiveness of artificial intelligence and the method of cross-correlation for recognition by subsurface radar, taking into account the additive noise of different levels present in practice. Materials and methods: The electrodynamic problem was solved by the finite difference time domain (FDTD) method. An artificial neural network (ANN) is trained on ideal signals to detect the features of the field that will be found in noisy data to determine to the position of the object. Cross-correlation also involves the use of an array of ideal signals, which will be correlated with noisy real signals. Results: The optimal and effective ANN structure for work with the received signals is created. It was tested for noise immunity. The recognition problem was also solved by the classical method of cross-correlation, and the influence of noise of different levels on its responses was studied. In addition, a comparison of the efficiency of their recognition using 1 and 4 sensors was made. Conclusions: For subsurface survey problems, a deep neural networks with at least three hidden layers of neurons should be used. This is due to the complexity and multidimensionality of the processes taking place in the surveyed space. It has been shown that artificial intelligence and cross-correlation techniques perform the object recognition well, and it is difficult to identify the best among them. Both approaches showed good noise immunity. The use of a larger data set of four receivers has a positive effect on the recognition results.

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UWB Technology for Detecting Alive People Behind Optically Opaque Obstacles

Cited by 3 publications

References 7 publications

Implementation of an Artificial Intelligence Approach to GPR Systems for Landmine Detection

Implementation of an Artificial Intelligence Approach to GPR Systems for Landmine Detection

Signal Processing in UWB Subsurface Radiolocation by Artificial Neural Networks

Comparison of subsurface object recognition by artificial neural networks and correlation method

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