Landmine detection and classification using UWB antenna system and ANN analysis

Dumin, Oleksandr; Pryshchenko, Oleksandr; Plakhtii, Vadym; Pochanin, Gennadiy

doi:10.1109/ukrmw49653.2020.9252574

Cited by 11 publications

(10 citation statements)

References 16 publications

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“…To solve the problems of automated classification and location recognition of underground objects, artificial neural networks are used [17][18][19][20]. However, the problem of filtering signal interference and signal amplification remains an unsolved issue.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…2. PMN-4 mine model [18] The process of propagation of nanosecuded electromagnetic pulses taking into account the features of the antenna system, electrophysical parameters of the soil is modeled by the method of finite differences in time domain (FDTD) [8]. The reflected wave from explosive objects and from the components of the receiving system is received by four antennas with different polarization orientation (Fig.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…3. Ultra-wideband radar, where the central antenna is the emitter; antennas at the edges -receivers with different polarization [18] This arrangement of antennas with different planes of polarization (Fig. 3) makes it possible to record the reflection of the wave at different points in time and indicate the direction from which the reflected wave came.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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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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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

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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“…It should be noted that the result of the neural network recognition can sign vary with high levels of additive Gaussian noise. These changes do not depen SNR level, but on the random realizations of the noise distribution in every data or on the parameters of the numerical simulations [12]. Therefore, to obtain a s generalization of ANN results we simulated 1000 random realizations of nois This effect is illustrated in Figure 10, where we compare the time shape of the signal received by one of the four antennas to that of a pre-processed signal.…”

Section: Description Of Underground Objectsmentioning

confidence: 99%

“…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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Collective Artificial Intellegence Approach for the Problem of Object Classification with UWB GPR

Pryshchenko

Dumin

Plakhtii

et al. 2021

2021 IEEE 26th International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory (DIPED

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Landmine detection and classification using UWB antenna system and ANN analysis

Cited by 11 publications

References 16 publications

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

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

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

Collective Artificial Intellegence Approach for the Problem of Object Classification with UWB GPR

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