Influence of denoising of input signal on classification of object location by artificial neural network in ultrawideband radiointroscopy

doi:10.26565/2311-0872-2019-31-03

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Commonly, UWB GPRs with "1Tx + 4Rx" antenna systems are used to get information about subsurface objects [21]. These radars irradiate the earth's surface with a soil dielectric constant of ε = 9 and a conductivity of σ = 0.005 S/m with nanosecond pulses [22] that penetrate several meters. In our case, we consider the PMN-1 (Fig.…”

Section: Methodsmentioning

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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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: Methodsmentioning

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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show abstract

“…To obtain information about underground objects, the ultra-wideband radars with antenna systems «1Tx+4Rx» (USA) are used [21]. These radars irradiate the surface of the earth with a soil dielectric constant of ε=9 and a conductivity of σ=0.005 cm/m with electromagnetic nanosecond pulses [22], which penetrate to a depth of several meters. For our task, the subsurface objects considered are the mines PMN-1 (USSR) PMN-4 mines.…”

Section: The Study Materials and Methodsmentioning

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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Detection and classification of landmines using UWB antenna system and ANN analysis

2020

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

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Background: The problem of detecting underground objects is found in many areas of human activity in the modern world, for example, a quick survey of the territory for the presence of underground utilities for earthworks, finding the location of grounding structures, cable breakage or short circuit, remote sensing for detecting and mapping of archaeological objects. The issue of humanitarian demining in Donetsk and Lugansk regions is also important in Ukraine. The latest ground surveying devices, such as ultrawideband subsurface radar, have already come to the aid for military sappers in developed countries to make the demining process safer. Objectives: The goal of this work is to improve the recognition of subsurface objects by using an artificial neural network (ANN) for signal processing, to test the influence of interference in signals coming from ultrawideband antenna system on the reliability of determining the object in the observation area, its type and distance to subsurface radar. Materials and methods: In this work, the ANN method is used to recognize the hidden objects by ultrawideband subsurface radar. The process of electromagnetic field propagation is simulated by finite time difference method (FDTD). Neural network testing is performed by adding Gaussian noise of different levels in the input signal. Simulation of the problem is performed 1000 times to exclude the randomness of recognition for different realizations of a noise. Results: Histograms of objects recognition for two types of mines and six types of cans were obtained. A large set of false objects for neural network training gave good results in the detection of antipersonnel mines, which was reflected in the excellent stability of determining the position and type of object, even in the presence of interference with a high signal-to-noise ratio. Conclusions: The problem of subsurface survey can be solved by using a fully connected neural network with five hidden layers of neurons. It has been determined that the use of artificial intelligence gives good results in the recognition of underground objects, if a high-quality learning data set for ANN will be prepared. Satisfactory stability of noisy signal operation is shown, which gives prospects for further testing of the developed method in application to a subsurface radar in the conditions of a real experiment.

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Influence of denoising of input signal on classification of object location by artificial neural network in ultrawideband radiointroscopy

Cited by 4 publications

References 19 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

Detection and classification of landmines using UWB antenna system and ANN analysis

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