To characterize atmospheric turbulence, the Doppler moments are estimated by weather radars. However, moment accuracy is highly sensitive to radar transmission parameters such as pulse repetition time (Ts) and number of pulses (Np), which affect Doppler ambiguity and estimation variance. Traditional fixed-parameter radars face trade-offs between aliasing and measurement precision. This paper proposes an adaptive radar framework that dynamically adjustsTsandNpon a per-scan basis to improve Doppler moment estimation at a single resolution cell level. Inspired by the Fully Adaptive Radar (FAR) concept, the method also includes a novel multi-lag Doppler width estimation scheme. Results demonstrate enhanced estimation accuracy, enabling better responsiveness to localized and non-stationary weather conditions.

The problem of precipitation detection using Frequency Modulated Continuous Wave (FMCW) radar under strong sectorial interference is addressed. The effect of such strong interferences in the case of an FMCW scanning radar is presented. Three signal-processing pipelines (two reflectivity-based and one Doppler-based) are proposed. The performances of all these pipelines are analyzed and compared. The morphology-based pipeline performs better for higher signal-to-noise ratios (> −15dB), whereas the entropy-based pipeline performs better in the case of lower SNRs (< −15dB). On the other hand, the circular variance-based masking technique is computationally very efficient. The proposed techniques are applied to simulated and real X-band fast-scanning radar data.

The problem of the limited accuracy of precipitation Doppler spectrum moments estimation measured by fast azimuthally scanning weather radars is addressed. A novel approach for the Doppler moment estimation based on maximum likelihood estimation is proposed. A simplified semianalytical parametric model for the precipitation power spectral density (PSD) as a function of the velocity parameters of the scatterers and the finite radar observation time is derived for typical precipitation-like weather conditions. An inverse problem for estimating the Doppler moments from measurements of the PSD is formulated and solved. It is demonstrated that the variance of the estimation of the Doppler moments approaches the Cramer Rao Lower Bound (CRB) when the observation time approaches infinity. The performance of the proposed approach is compared with some classical techniques and another realization of the maximum likelihood approach based on simulated and experimental data. The results indicate the superiority of the proposed approach, especially for short observation time. Furthermore, a scanning strategy to accurately estimate the Doppler moments based on the true velocity dispersion of the scatterers is provided with the help of the proposed approach.

The challenge of avoiding aliasing in the Doppler spectrum for precipitation is addressed. A novel integrative signal processing approach has been proposed to address the research gaps from various disciplines. The proposed approach consists of several steps. First, an aperiodic way of sampling the echoes (aperiodic sampling refers to aperiodic pulse train in the context of radar echoes in slow time) has been proposed by which the maximum unambiguous Doppler frequency (velocity) is enhanced. Second, the Doppler spectrum moment estimation is performed with the help of a parametric form of its covariance. The performance of the moment estimation is assessed by the bias and the variance in the estimated counterparts. The theoretical variance for the parameter estimation is also derived. An aperiodic pulse train design recommendation has been proposed for adequately and unambiguously estimating the Doppler moments for one extended target (like precipitation). Finally, a spectrum reconstruction technique is implemented after the moment estimation on simulated radar echo samples for a realistic precipitation-like event. The comparison with the other approaches proves its superiority for parameter estimation and Doppler spectrum reconstruction.

In this study, the problem of multipath in radar sensor networks for human activity recognition (HAR) has been examined. Traditionally considered as a source of additional clutter, the multipath is being investigated for its potential to be exploited through the creation of virtual radar nodes. These virtual nodes are conceptualized to observe targets from aspect angles that differ from those of physically existing radars. To realize this idea, an innovative processing pipeline is proposed that extracts information from multipath signals to improve HAR. The pipeline isolates and tracks the line-of-sight (LOS) and multipath components of a moving human target performing continuous sequences of activities observed by a network of 3 radar sensors. Furthermore, the method has been verified with experimental data consisting of 6 activities and 14 volunteers by comparing classification metrics with the use of a single radar as well as only the LOS components of the 3 radars in the network. A 12-layer convolutional neural network (CNN) classifier has been designed to operate on range-Doppler (RD) images derived from the LOS and multipath components, extracted by the proposed method. A substantial performance improvement using theleave-one-person-outtest set is demonstrated in the order of + 11% by exploiting a multi-radar network with its LOS and multipath components.

The problem of radar-based counting of multiple individuals moving as a single group is addressed using an mm-wave multiple-input–multiple-output (MIMO) frequency-modulated continuous wave (FMCW) radar. This problem is challenging because the different individuals are closer to each other than the range/azimuth resolution, and their bulk Doppler signatures are difficult to distinguish, as they tend to move together. A processing pipeline is proposed, based on the combination of a multiple target tracking algorithm with a classifier to track each group and count the number of people within. Specific salient features are defined for the classifier and extracted from range–azimuth maps and cadence velocity diagrams (CVDs). The proposed pipeline has been experimentally validated in several outdoor scenarios with grouped people. The results show that the combination of tracking algorithm and classifier in the proposed pipeline outperforms alternative methods from the literature as well as a commercial toolbox for people counting.

Unconstrained human activities recognition with a radar network is considered. A hybrid classifier combining both CNNs and RNNs for spatial-temporal pattern extraction is proposed. The two-dimensional CNNs (2D-CNNs) are first applied to the radar data to perform spatial feature extraction on the input spectrograms. Subsequently, gated recurrent units with bidirectional implementations are used to capture the long- and short-term temporal dependencies in the feature maps generated by the 2D-CNNs. Three NN-based data fusion methods were explored and compared to utilize the rich information provided by the different radar nodes. The performance of the proposed classifier was validated rigorously using the K-fold CV and L1PO method. Unlike competitive research, the dataset with continuous human activities with seamless inter-activity transitions that can occur at any time and unconstrained moving trajectories of the participants has been collected and used for evaluation purposes. Classification accuracy of about 90.8% is achieved for nine-class HAR by the proposed classifier with the halfway fusion method.

The performance of deep learning (DL) algorithms for radar-based human motion recognition (HMR) is hindered by the diversity and volume of the available training data. In this article, to tackle the issue of insufficient training data for HMR, we propose an instance-based transfer learning (ITL) method with limited radar micro-Doppler (MD) signatures, alleviating the burden of collecting and annotating a large number of radar samples. ITL is a unique algorithm that consists of three interconnected parts, including DL model pretraining, correlated source data selection, and adaptive collaborative fine-tuning (FT). Any of the three components cannot be excluded; otherwise, the performance of the entire algorithm decreases. The experiments with a radar data set of six human motions show that ITL achieves state-of-the-art performance for HMR with limited training samples, outperforming several existing transfer learning approaches. Especially, when there are only 100 samples per person per class, ITL yields an F1 score of 96.7%. Last but not least, ITL is more generalized to human motion differences. Though adapted to recognize the persons’ motions in a small-scale target data set, ITL can also classify the persons’ motion data used for pretraining, achieving up to 11.0% F1 score enhancement over the conventional FT method.

The sensor resource management problem in a multi-object tracking scenario is considered. In order to solve it, a dynamic budget balancing algorithm is proposed which models the different sensor tasks as partially observable Markov decision processes. Those are being solved by applying a combination of Lagrangian relaxation and policy rollout. The algorithm converges to a solution which is close to the optimal steady-state solution. This is shown through simulations of a two-dimensional tracking scenario. Moreover, it is demonstrated how the algorithm allocates the sensor time budgets dynamically to a changing environment and takes predictions of the future situation into account.

The future fifth generation (5G) systems will aim to design low‐cost phased array base station antenna systems at mm‐waves for simultaneous multiple beamforming with enhanced spatial multiplexing, limited interference, acceptable power consumption, suitable processing complexity, and passive cooling. In this study, a multi‐user space division multiple access (SDMA) model is developed to investigate the trade‐off between the quality of service (QoS), computational complexity in beamforming and cooling requirements for various use cases, and a number of users. The QoS at the user ends is rated by assessing the statistical signal‐to‐interference‐plus‐noise ratios (SINRs). Two beamforming algorithms, namely conjugate beamforming (CB) and zero‐forcing (ZF), are considered and compared. Depending on the deployment scenario, rotated and optimised array layouts are proposed to be used in CB with the least computational complexity while providing relatively good QoS. Different reduced‐complexity ZF algorithms are introduced as a compromise between the SINR performance and computational burden. The impact of the number of simultaneously served users on the thermal management in active integrated 5G base station antenna arrays is investigated as well.

3-D imaging with irregular planar multiple-input-multiple-output (MIMO) arrays is discussed. Due to signal acquisition on irregular spatial sampling grids by using these antenna arrays, the fast Fourier transform (FFT)-based imaging algorithms cannot readily be used for image formation. To avoid the application of computationally intensive coherent summation algorithms such as filtered backprojection or Kirchhoff migration, we propose a nonuniform FFT (NUFFT)-based MIMO Range Migration Algorithm (i.e., NUFFT-based MIMO-RMA) for efficient microwave imaging. The algorithm exploits NUFFT to reconstruct the wavenumber–domain spectra related to each Fourier frequency. It is generic and applicable to 3-D imaging with irregular planar MIMO arrays. The effects of irregular spatial sampling and signal bandwidth on the imaging performance and computational efficiency of the proposed algorithm are analyzed. Finally, some numerical simulations and experimental results are presented to demonstrate the performance of the proposed imaging algorithm.

The work investigates staggered and random PRF (Pulse Repetition Frequency) strategies for a close formation of small Synthetic Aperture Radar (SAR) satellites operating in a multistatic configuration. The satellites are positioned within a fraction of the along-track critical baseline, hence allowing for the application of Displaced Phase Center image formation approaches. The performance of regular and random pulse sampling schemes is in particular assessed for a single-input multiple-output (SIMO) S-Band constellation, whose feasibility is further analyzed in relation to the number of satellites and their antenna size.

The growing need for high-performance imaging tools for terrorist threat detection and medical diagnosis has led to the development of new active architectures in the microwave and millimeter range. Notably, multiple-input multiple-output systems can meet the resolution constraints imposed by these applications by creating large, synthetic radiating apertures with a limited number of antennas used independently in transmitting and receiving signals. However, the implementation of such systems is coupled with strong constraints in the software layer, requiring the development of reconstruction techniques capable of interrogating the observed scene by optimizing both the resolution of images reconstructed in two or three dimensions and the associated computation times. In this paper, we first review the formalisms and constraints associated with each application by taking stock of efficient processing techniques based on spectral decompositions, and then, we present a new technique called the transverse spectrum deconvolution range migration algorithm allowing us to carry out reconstructions that are both faster and more accurate than with conventional Fourier domain processing techniques. This paper is particularly relevant to the development of new computational imaging tools that require, even more pronouncedly than in the case of conventional architectures, fast image computing techniques despite a very large number of radiating elements interrogating the scene to be imaged.

Ground penetrating radar imaging from the data acquired with arbitrarily oriented dipole-like antennas is considered. To take into account variations of antenna orientations resulting in spatial rotation of antenna radiation patterns and polarizations of transmitted fields, the full-wave method that accounts for the near-, intermediate-, and far-field contributions to the radiation patterns is applied for image reconstruction, which is formulated as a linear inversion problem. Two approaches, namely, an interpolation-based method and a nonuniform fast Fourier transform-based method, are suggested to efficiently implement the full-wave method by computing exact Green’s functions. The effectiveness and accuracy of the method proposed have been verified via both numerical simulations and experimental measurements, and significant improvement of the reconstructed image quality compared with the traditional scalar-wave-based migration algorithms is demonstrated. The results can be directly utilized by forward-looking microwave imaging sensors such as installed at tunnel boring machine or can be used for the observation matrix computation in regularization-based inversion algorithms.

In this paper, a wavenumber-domain matrix-pencil-based multiband signal fusion approach was proposed for multiband microwave imaging. The approach proposed is based on the Born approximation of the field scattered from a target resulting in the fact that in a given scattering direction, the scattered field can be represented over the whole frequency band as a sum of the same number of contributions. Exploiting the measured multiband data and taking advantage of the parametric modeling for the signals in a radial direction, a unified signal model can be estimated for a large bandwidth in the wavenumber domain. It can be used to fuse the signals at different subbands by extrapolating the missing data in the frequency gaps between them or coherently integrating the overlaps between the adjacent subbands, thus synthesizing an equivalent wideband signal spectrum. Taking an inverse Fourier transform, the synthesized spectrum results in a focused image with improved resolution. Compared with the space–time domain fusion methods, the proposed approach is applicable for radar imaging with the signals collected by either collocated or noncollocated arrays in different frequency bands. Its effectiveness and accuracy are demonstrated through both numerical simulations and experimental imaging results.

One of the main computational drawbacks in the application of 3-D iterative inversion techniques is the requirement of solving the field quantities for the updated contrast in every iteration. In this paper, the 3-D electromagnetic inverse scattering problem is put into a discretized finite-difference frequency-domain scheme and linearized into a cascade of two linear functionals. To deal with the nonuniqueness effectively, the joint structure of the contrast sources is exploited using a sum-of-l1-norm optimization scheme. A cross-validation technique is used to check whether the optimization process is accurate enough. The total fields are, then, calculated and used to reconstruct the contrast by minimizing a cost functional defined as the sum of the data error and the state error. In this procedure, the total fields in the inversion domain are computed only once, while the quality and the accuracy of the obtained reconstructions are maintained. The novel method is applied to ground-penetrating radar imaging and through-the-wall imaging, in which the validity and the efficiency of the method are demonstrated.

In this paper, a track selection problem for multi-target tracking in a multifunction radar network is studied using the concepts from game theory. The problem is formulated as a non-cooperative game, and specifically as an anti-coordination game, where each player aims to differ from what other players do. The players' utilities are modeled using a proper tracking accuracy criterion and, under different assumptions on the structure of these utilities, the corresponding Nash equilibria are characterized. To find an equilibrium, a distributed algorithm based on the best-response dynamics is proposed. Finally, computer simulations are carried out to verify the effectiveness of the proposed algorithm in a multi-target tracking scenario.

Ground penetrating radar (GPR) using a 3D antenna array is employed for data acquisition over an area contaminated with landmine simulants. The performance of different processing methods applied in 1D, 2D and 3D radiograms are evaluated on real data obtained from two different test sites. Preliminary results are presented on the effectiveness of 3D GPR and on the applicability and limitations of widely used processing schemes.

A regression model is developed in order to estimate in real time the signal-to-clutter ratio (SCR) for landmine detection using ground-penetrating radar. Artificial neural networks are employed in order to express SCR with respect to the soil's properties, the depth of the target, and the central frequency of the pulse. The SCR is synthetically evaluated for a wide range of diverse and controlled scenarios using the finite-difference time-domain method. Fractals are used to describe the geometry of the soil's heterogeneities as well as the roughness of the surface. The dispersive dielectric properties of the soil are expressed with respect to traditionally used soil parameters, namely, sand fraction, clay fraction, water fraction, bulk density, and particle density. Through this approach, a coherent and uniformly distributed training set is created. The overall performance of the resulting nonlinear function is evaluated using scenarios which are not included in the training process. The calculated and the predicted SCR are in good agreement, indicating the validity and the generalization capabilities of the suggested framework.

A novel algorithm is proposed for separating multiple moving targets in radar images in the slow time-range domain. Target discrimination is based on an image texture angle that is related to the target's instantaneous velocity. The algorithm efficiency has been successfully verified for targets with variable velocities.

This paper introduces a fast imaging algorithm oriented for linear MIMO array configurations. The image reconstruction process is performed in the frequency-wavenumber domain and requires a modified interpolation process among both transmit and receive apertures. The proposed algorithm corrects completely the range curvature in the near-field and is able to provide high quality images for short-range targets. The proposed algorithm is tested by numerical simulation and further compared with Stolt migration where antenna positions are approximated by phase centers between bi-static transceiver pairs. The results demonstrate its high accuracy and computational efficiency for near-field MIMO array imaging.

This paper presents a 3-D near-field imaging algorithm that is formulated for 2-D wideband multiple-input-multiple-output (MIMO) imaging array topology. The proposed MIMO range migration technique performs the image reconstruction procedure in the frequency-wavenumber domain. The algorithm is able to completely compensate the curvature of the wavefront in the near-field through a specifically defined interpolation process and provides extremely high computational efficiency by the application of the fast Fourier transform. The implementation aspects of the algorithm and the sampling criteria of a MIMO aperture are discussed. The image reconstruction performance and computational efficiency of the algorithm are demonstrated both with numerical simulations and measurements using 2-D MIMO arrays. Real-time 3-D near-field imaging can be achieved with a real-aperture array by applying the proposed MIMO range migration techniques.

A high-resolution imaging system based on the combination of ultrawideband (UWB) transmission, multiple-input-multiple-output (MIMO) array, and synthetic aperture radar (SAR) is suggested and studied. Starting from the resolution requirements, spatial sampling criteria for nonmonochromatic waves are investigated. Exploring the decisive influence of the system's fractional bandwidth (instead of previously claimed aperture sparsity) on the imaging capabilities of sparse aperture arrays, a MIMO linear array is designed based on the principle of effective aperture. For the antenna array, an optimized UWB antenna is designed allowing for distortionless impulse radiation with more than 150% fractional bandwidth. By combining the digital beamforming in the MIMO array with the SAR in the orthogonal direction, a high-resolution 3-D volumetric imaging system with a significantly reduced number of antenna elements is proposed. The proposed imaging system is experimentally verified against the conventional 2-D SAR under different conditions, including a typical concealed-weapon-detection scenario. The imaging results confirm the correctness of the proposed system design and show a strong potential of the MIMO-SAR-based UWB system for security applications.

Data acquisition speed is an inherent problem of stepped-frequency continuous-wave (SFCW) radars, which may discourage further usage and development of this technology. We propose an emerging paradigm called compressed sensing (CS) to overcome this problem. In CS, a signal can be reconstructed exactly based on only a few samples below the Nyquist rate. Accordingly, the data acquisition speed can be increased significantly. A novel design of an SFCW ground-penetrating radar (GPR) with high acquisition speed is proposed and evaluated. Simulation by a monocycle waveform and actual measurement by a vector network analyzer at a GPR test range indicate the applicability of the proposed system.

Detection of trapped victims using ultrawideband radar is considered a highly challenging task due to multiple unknown parameters and generally very low signal-to-noise-and-clutter ratio (SNCR) conditions. In this paper, we propose a novel detection algorithm which is designed for detection of periodic motion caused by, e.g., respiratory motion of the victim for low SNCR conditions. The aim is to separate the respiratory-motion response of a trapped victim from nonstationary clutter originating from moving objects in the scene of interest. The algorithm performs stationary-clutter removal, high-level noise, and nonstationary-clutter suppression, indicates presence of the trapped victim, and estimates its range. The performance of the algorithm is investigated, both by means of simulation and experimental verification. The results show improved detection capabilities in low SNCR over an existing algorithm proposed by Zaikov

In the above paper (this issue, pp. 2005-2014), there is an error in the top line of the right column, page 2010, which is corrected in this paper.

In this paper, the formulation of Kirchhoff migration is modified for multiple-input-multiple-output (MIMO) array-based radar imaging in both free-space and subsurface scenarios. By applying the Kirchhoff integral to the multistatic data acquisition, the integral expression for the MIMO imaging is explicitly derived. Inclusion of the Snell's law and the Fresnel's equations into the integral formulation further expends the migration technique to subsurface imaging. A modification of the technique for strongly offset targets is proposed as well. The developed migration techniques are able to perform imaging with arbitrary MIMO configurations, which allow further exploration of the benefits of various array topologies. The proposed algorithms are compared with conventional diffraction stack migration on free-space synthetic data and experimentally validated by ground-penetrating radar experiments in subsurface scenarios. The results show that the modified Kirchhoff migration is superior over the conventional diffraction stack migration in the aspects of resolution, side-lobe level, clutter rejection ratio, and the ability to reconstruct shapes of distributed targets.

Wireless personal area networks applications may benefit from the use of ultra-wideband (UWB) technology. In these applications transmit and receive antennas are very close to each other and the far-field condition assumed in most of the link budget models may not be satisfied. Under near- field conditions, variations in the link budget and pulse shape compared to the far-field can be observed. In this work, a new UWB link budget model for very short distances is proposed and validated with measurements using different types of antennas. The measurements have been performed in the time domain by exciting the antenna with very short pulse of 35 ps width covering a large frequency band. The proposed model, which includes frequency, antenna size and orientation as parameters, shows a good agreement with the simulations and the measurements.

A polarimetric multi-feature framework for the detection of antipersonnel landmines with Ground Penetrating Radar (GPR) is suggested. The features result from independently acquired and processed GPR measurements in co- and cross- polar configurations. The initial detection in the confidence maps is made independently after which the coordinates of the detected targets are co-located. The marginal feature distributions are normalized via Johnson’s transform prior to the fusion process and a Maximum Likelihood based linear-quadratic classifier is used as a fusion rule. The framework makes use of secondary data acquired from an open test site to train the classifier. The framework performance is illustrated on the data acquired over a specifically designed test- site.

The 17 papers in this special issue focus on subsurface sensing using ground-penetrating radar (GPR). These papers describe new approaches to subsurface sensing, including developments in electromagnetic wave propagation imaging/inversion of GPR data and antenna/radar technologies. Associated applications range from glaciology to pavement evaluation to landmine detection.

In this paper, we propose a new algorithm for the enhancement of plastic-cased antipersonnel mine detection using a video-impulse ground-penetrating radar (GPR). The algorithm is implemented as a nonlinear signal processor, which searches for the presence of a reference waveform in a 1D GPR echo return. The reference waveform represents a class of targets within a certain environment. The processor marks the presence of all responses similar to the reference waveform with a sharp mono-cycle. Simultaneously, responses with different waveforms, which presumably correspond to clutter, are suppressed. The reference waveform and other algorithm parameters are determined from training data sets acquired in a controlled environment. After training, the algorithm can be successfully applied at sites where soil, targets, and measurement scenarios are similar but not identical to those of the training site. The processor is integrated into an automated data processing and mine detection scheme as an additional clutter suppression step. The scheme consists of clutter suppression, synthetic aperture radar focusing, construction of a confidence map, and automated detection in it. The suggested algorithm is tested on experimental data, and its performance is compared against schemes where clutter suppression is organized by means of background removal and the cross correlation with a reference wavelet. The performance comparison is done in terms of receiver operating characteristic curves. It has been found that the suggested algorithm reduces the false alarm rate in about two and a half times in comparison to the cross-correlation-based clutter suppression.

A UWB antenna, which is adopted from a ground penetrating radar (GPR) antenna, is proposed for impulse radio application. The antenna has dimensioning of 7 cm by 1.5 cm and is based on a modified bow-tie structure. It employs improved resistive loading for transmission of 0.2-ns monocycles with minimal ringing. The antenna radiation efficiency is enhanced by creating discontinuities in the antenna that serve as secondary sources of radiation. Numerical analysis of the antenna was performed using the FDTD method and the method of moments. It has been found that antenna ringing is adequately suppressed after less than 3 times the pulse radiation. This will set the potential limit of the data rate at more than 1 Gbit/s. Furthermore, the antenna shows a relatively flat gain and input impedance in the 3 - 10 GHz frequency range. Within this bandwidth the antenna is omni-directional and has stable radiation pattern

In this paper the basic design of an adaptive GPR antenna is introduced. The antenna is able to adapt its input impedance to a variation in the antenna elevation and soil type to keep reflections at the antenna's terminal minimum. As a result, the energy radiated by the antenna into the ground for different antenna elevations and soil types would be maximized. The antenna is based on a wire bow-tie structure with variable flare angle for adjusting the antenna's input impedance. The flare angle variation is realized by short-circuiting the gaps which separate the wires from the antenna feed point. Electronic switching devices such as PIN diodes could be used to allow fast and convenient control of the antenna's flare angle.

The clutter caused by scattering from a rough air-ground interface is analyzed numerically. The simulations have been done using Monte Carlo approach. Statistical properties of the scattered field have been analyzed. It has been found that the phase of the reflected electromagnetic field nearby a rough air-ground interface follows the profile of the interface and this phase modulation of the reflected field is responsible for the forming of the clutter. Furthermore it is demonstrated that the correlation function of the phase of the reflected field coincides with the correlation function of the rough surface, while for the correlation of the reflected field it is valid only if the magnitude of the surface clutter is considerably less than the mean value of the ground reflection.

We performed careful analysis of the measure data to determine the clutter level and responses of different targets in different types of soils under different environmental conditions. We found that even in a dry sand ground clutter level is higher than responses of many types of antipersonnel mines. As a result, the signal-to-clutter ratio for GPR targets becomes nonsufficient for reliable detection by widely used detection procedures. A new detection algorithm, which should partly overcome this problem, has been developed. The performance of the algorithm on real GPR data is encouraging.

A full-polarimetric ultra wideband GPR front-end has been developed in IRCTR especially for landmine detection application. A number of new ideas have been implemented in the design. A principally new antenna system design and an ability to perform quasi-simultaneous measurements with two transmit polarization and in two different frequency bands are main novelty aspects of the radar. Additionally in comparison with commercially available video impulse GPR systems the front-end has considerably larger bandwidth, ability to measure polarimetric structure of the scattered field and very high precision of scattered field measurements.