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.

To design holographic and impulse ground penetrating radar (GPR) sensors suitable for humanitarian de-mining in the Donbass (Ukraine) conflict zone, we measured critical electromagnetic parameters of typical local soils using simple methods that could be adapted to any geologic setting. Measurements were recorded along six profiles, each crossing at least two mapped soil types. The parameters selected to evaluate GPR and metal detector sensor performance were magnetic permeability, electrical conductivity, and dielectric permittivity. Magnetic permeability measurements indicated that local soils would be conducive to metal detector performance. Electrical conductivity measurements indicated that local soils would be medium to high loss materials for GPR. Calculation of the expected attenuation as a function of signal frequency suggested that 1 GHz may have optimized the trade-off between resolution and penetration and matched the impulse GPR system power budget. Dielectric permittivity was measured using both time domain reflectometry and impulse GPR. For the latter, a calibration procedure based on an in-situ measurement of reflection coefficient was proposed and the data were analyzed to show that soil conditions were suitable for the reliable use of impulse GPR. A distinct difference between the results of these two suggested a dry (low dielectric) soil surface, grading downward into more moist (higher dielectric) soils. This gradation may provide a matching layer to reduce ground surface reflections that often obscure shallow subsurface targets. In addition, the relatively high dielectric deeper (10 cm–20 cm) subsurface soils should provide a strong contrast with plastic-cased mines.

The results of development, tests and application of the GPR for measurement of the road asphalt layers thickness are presented in the paper. Main necessary improvements providing the possibility of use the GPR as a measuring device are described as well as some results of application of the GPR for inspection of the road structure after reparation the road. It was achieved accuracy of layers thickness measurement better than 0.5 cm.

This paper discusses modern requirements for, and approaches to, design of a multi-sensor robotic radar system for humanitarian demining, and a through-wall imaging radar. In addition, we review progress in use of UWB radar systems for investigation of objects and structures representing important historical and cultural heritage. A review of the special European action intended for progress in civil application of UWB radar is also presented.

The problem of identification of objects under ground surface is solved by the application of irradiation of the surface by short impulse electromagnetic waves and the use of artificial neural networks (ANN) for the analysis of reflected field characteristics. As input data for ANN the normalized amplitudes of electrical component of the field in determined points of observation in equidistant moments of time are used. As an example of the object for the identification, the metal tube under surface of a ground is considered. The plane electromagnetic wave having Gaussian time dependence is used as an incident field. The influence of a number of hidden layers of ANN on precision of the recognition is investigated.