This study proposes a low-complexity permittivity estimation for ground penetrating radar applications based on a contrast source inversion (CSI) approach, assuming multilayered ground media. The homogeneity assumption for each background layer is used to address the ill-posed condition while maintaining accuracy for permittivity reconstruction, significantly reducing the number of unknowns. Using an appropriate initial guess for each layer, the post-CSI approach also provides the dielectric profile of a buried object. The finite difference time domain numerical tests show that the proposed approach significantly enhances reconstruction accuracy for buried objects compared with the traditional CSI approach.

In this paper, we propose a cross-correlation method applied to multistatic ground penetrating radar (GPR) data sets to detect road pavement damage. Pavement cracks and delamination cause variations in electromagnetic wave propagation. The proposed method can detect velocity change using cross-correlation of data traces at different times. An artificially damaged airport taxiway model was measured, and the method captures the positions of damaged parts.

This paper reports the development of a landmine visualization system based on complex-valued self-organizing map (CSOM) by employing one-dimensional (1-D) array of taper-walled tapered slot antennas (TSAs). Previously we constructed a high-density two-dimensional array system to observe and classify complex-amplitude texture of scattered wave. The system has superiority in its adaptive distinction ability between landmines and other clutters. However, it used so many (144) antenna elements with many mechanical radio-frequency (RF) switches and cables that it has difficulty in its maintenance and also requires long measurement time. The 1-D array system proposed here uses only 12 antennas and adopts electronic RF switches, resulting in easy maintenance and 1/4 measurement time. Though we observe stripe noise specific to this 1-D system, we succeed in visualization with effective solutions.

We propose an effective technique for estimation of targets by ground penetrating radar (GPR) using model-based compressive sensing (CS). We demonstrate the technique's performance by applying it to detection of buried landmines. The conventional CS algorithm enables the reconstruction of sparse subsurface images using much reduced measurement by exploiting its sparsity. However, for landmine detection purposes, CS faces some challenges because the landmine is not exactly a point target and also faces high level clutter from the propagation in the medium. By exploiting the physical characteristics of the landmine using model-based CS, the probability of landmine detection can be increased. Using a small pixel size, the landmine reflection in the image is represented by several pixels grouped in a three dimensional plane. This block structure can be used in the model based CS processing for imaging the buried landmine. The evaluation using laboratory data and datasets obtained from an actual mine field in Cambodia shows that the model-based CS gives better reconstruction of landmine images than conventional CS.

A method of calibration for GPR responses is introduced in order to extract a target response from GPR data. This calibration procedure eliminates undesirable waveform distortion that is caused by antenna characteristics and multiple scattering effects between the antennas and the ground surface. An application result to measured GPR data shows that undesirable late-time responses caused by the antenna characteristics and multiple scattering effects are removed, and that the target response is clearly reconstructed. This result demonstrates that the waveform calibration of GPR data is significant and essential for reliable target identification.

This brief paper proposes a method for calibration of GPR pulse waveforms that is effective for identification of buried objects in the ground and/or in concrete structures. This approach is based on the inverse filtering operation that eliminates the influence of GPR antenna characteristics, and a response from a flat metal plate is employed as a reference data for calibration. In order to evaluate the effectiveness of this approach, it is applied to actual experimental data measured by the UWB-GPR antennas. The results show the validity of the method and importance of the waveform calibration for target identification.

A method for reducing ground clutter contribution from ground penetrating radar (GPR) data is proposed for discrimination of landmines located in shallow depth. The algorithm of this method is based on the Matching Pursuit (MP) that is a technique for non-orthogonal signal decomposition using dictionary of functions. As the dictionary of function, a wave-based dictionary constructed by taking account of scattering mechanisms of electromagnetic (EM) wave by rough surfaces is employed. Through numerical simulations, performance of ground clutter reduction is evaluated. The results show that the proposed method has good performance and is effective for GPR data preprocessing for discrimination of shallowly buried landmines.

The complex-valued self-organizing map (CSOM) realizes an adaptive distinction between plastic landmines and other objects in landmine visualization systems. However, when the spatial resolution in electromagnetic-wave measurement is not sufficiently high, the distinction sometimes fails. To solve this problem, in this paper, we propose two techniques to enhance the visualization ability. One is the utilization of SOM-space topology in the CSOM adaptive classification. The other is a novel feature extraction method paying attention to local correlation in the frequency domain. In experimental results, we find that these two techniques significantly improve the visualization performance. The local-correlation method contributes also to the reduction of the number of tuning parameters in the CSOM classification.

A feature for classification of shallowly buried landmine-like objects using a ground penetrating radar (GPR) measurement system is proposed and its performance is evaluated. The feature for classification employed here is a time interval between two pulses reflected from top and bottom sides of landmine-like objects. First, we estimate a time resolution required to detect object thickness from GPR data, and check the actual time resolution through laboratory experiment. Next, we evaluate the classification performance using Monte Carlo simulations from dataset generated by a two-dimensional finite difference time domain (FDTD) method. The results show that good classification performance is achieved even for landmine-like objects buried at shallow depths under rough ground surfaces. Furthermore, we also estimate the effects of ground surface roughness, soil inhomogeneity, and target inclination on the classification performance.

A method for detecting shallowly buried landmines using sequential ground penetrating radar (GPR) data is presented. After removing a dominant coherent component arising from the ground surface reflection from the GPR data, three kinds of target features related to wave correlation, energy ratio, and signal arrival time are extracted. Since the detection problem treated here is reduced to a binary hypothesis test, an approach based on a likelihood ratio test is employed as a detection algorithm. In order to check the detection performance, a Monte Carlo simulation is carried out for data generated by a two-dimensional finite-difference time domain (FDTD) method. Results given in the form of receiver operating characteristic (ROC) curves show that good detection performance is obtained even for landmines buried at shallow depths under rough ground surfaces, where the responses from the landmines and that from the ground surface overlap in time.

A two-dimensional algorithm, which combines the well-known Synthetic Aperture Radar (SAR) imaging and the recently developed effective inversion method, is presented and applied to a three-dimensional configuration. During the first stage a two-dimensional image of a realistic three-dimensional buried object is obtained. In the second stage the average permittivity of the object is estimated using a two-dimensional effective inversion scheme where the geometrical information retrieved from the SAR image is employed. The algorithm is applicable in real time.