Application of UWB Electromagnetic Waves for Subsurface Object Location Classification by Artificial Neural Networks

Dumin, Oleksandr; Pryshchenko, Oleksandr; Shyrokorad, Dmytro; Plakhtii, Vadym

doi:10.1109/uwbusis.2018.8520097

Cited by 13 publications

(2 citation statements)

References 10 publications

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“…Повноз'єднана структура ШНМ була обрана через те, що для подібного класу задач не було помічено серйозного впливу структури на результати розпізнавання [23]. Розв'язання задачі має бути складнішим порівняно з [24] через використання чотирьох сигналів та меншу енергію відбитого поля тривимірним досліджуваним об'єктом, в той час як двовимірний об'єкт був успішно розпізнаний у попередніх роботах, наприклад у [24].…”

Section: постановка задачіunclassified

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

2019

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

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Background: A wide usage of impulse ultrawideband subsurface radars for a number of practical approaches in archeology, construction and humanitarian demining is holding back because of presence of noises and clutters of high level in the reflected field. It often makes the object classification practically unreal for at not big depths and distances from receiving and transmitting antennas. Besides of using special antenna system designs to improve recognition results, it is interesting to apply modern digital signal filtering techniques. Objectives: To investigate the influence of denoising on the quality of artificial neural network recognition of subsurface objects and their coordinates for a model of additive gaussian noise of a different noise level. Materials and methods: In this paper the idea of improving the stability of recognition of hidden objects in the presence of outside noise by previous processing of input signals with the latest popular noise reduction methods, such as the caterpillar method and wavelet transform method is verified. To eliminate the randomness of the result of the neural network response for each realization of the additive noise of a given level, a sufficient number of attempts are calculated for each of the methods, and statistics are provided to illustrate the effectiveness of each of the approaches. To check the hypothesis of the efficiency of input signal denoising the numerical simulation of the model of a real ground surface with subsurface object is carried out by means of Finite Difference Time Domain method (FDTD). The artificial neural network is trained on the obtained ideal time dependences of the amplitudes of the reflected field to correctly recognize the position of the object. The training is subsequently checked on the same input signals with additional noise of a certain level. Recognition errors in the last case are compared with similar errors when popular noise reduction procedures are applied to noisy input signals. Results: It is demonstrated that artificial neural networks have good approximating properties capable to effectively resist the noises in the input signals It is shown that for all noise levels, the caterpillar method statistically degrades the quality of an object recognition. The wavelet-transform method statistically improves slightly the classification of objects than for absence of denoising, but this result is not stable. Conclusion: For effective application of methods of noise filtration in received signals of impulse radar it is nessusary to have previous knowledge about noise character or peculiarities of useful signal. Implementation of denoising techniques without the use of this knowledge cannot improve the recognition quality of surface objects.

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Section: постановка задачіunclassified

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

2019

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

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“…In our case, the training dataset can be collected from the reflected impulse signals [29]. The more complex the object under investigation is, the more training data we require for identification [30]. Fortunately, many studies have shown the stability of ANN in working with noisy signals [31][32][33][34].…”

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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Subsurface Object Recognition in a Soil Using UWB Irradiation by Butterfly Antenna

Persanov

Dumin

Plakhtii

et al. 2019

2019 XXIVth International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory (DIPED)

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Application of UWB Electromagnetic Waves for Subsurface Object Location Classification by Artificial Neural Networks

Cited by 13 publications

References 10 publications

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

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

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

Subsurface Object Recognition in a Soil Using UWB Irradiation by Butterfly Antenna

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