Olga Lopera

Filtering Soil Surface and Antenna Effects From GPR Data to Enhance Landmine Detection

The detection of antipersonnel landmines using ground-penetrating radar (GPR) is particularly hindered by the predominant soil surface and antenna reflections. In this paper, we propose a novel approach to filter out these effects from 2-D off-ground monostatic GPR data by adapting and combining the radar antenna subsurface model of Lambot with phase-shift migration. First, the antenna multiple reflections originating from the antenna itself and from the interaction between the antenna and the ground are removed using linear transfer functions. Second, a simulated Greens function accounting for the surface reflection is subtracted. The Greens function is derived from the estimated soil surface dielectric permittivity using full-wave inversion of the radar signal for a measurement taken in a local landmine-free area. Third, off-ground phase-shift migration is performed on the 2-D data to filter the effect of the antenna radiation pattern. We validate the approach in laboratory conditions for four differently detectable landmines embedded in a sandy soil. Compared to traditional background subtraction, this new filtering method permits a better differentiation of the landmine and estimation of its depth and geometrical properties. This is particularly beneficial for the detection of landmines in low-contrast conditions

Combination of Advanced Inversion Techniques for an Accurate Target Localization via GPR for Demining Applications

We used advanced ground-penetrating radar (GPR) inversion techniques for detecting landmines in laboratory conditions. The radar data were acquired with a calibrated vector network analyzer combined with an off-ground monostatic horn antenna, thereby setting up a stepped-frequency continuous-wave radar. Major antenna effects and interactions with the soil and targets were filtered out using frequency-dependent complex antenna transfer functions. The proposed strategy first exploits inversion approaches that are able to give an accurate characterization of the antenna-soil interaction and a reliable estimate of the soil permittivity. The outcomes of this first phase are at the basis of the application of a microwave tomographic approach based on the Born approximation to achieve the imaging of the subsurface. The algorithms were applied for imaging three landmines of different sizes and buried at different depths in sand. Although the radar system was off the ground, the results showed that it was possible to reconstruct all mines, including a shallow plastic mine as small as 5.6 cm in diameter. This last mine was invisible in the raw radar data, and the use of common GPR imaging techniques did not lead to satisfactory results. The proposed integrated method shows great promise for shallow subsurface imaging in a demining context, particularly because it automatically provides accurate information on the shallow soil dielectric permittivity.

ESTIMATION OF RELATIVE PERMITTIVITY OF SHALLOW SOILS BY USING THE GROUND PENETRATING RADAR RESPONSE FROM DIFFERENT BURIED TARGETS

Combined ground penetrating radar and metal detector equipment are now avail- able (e.g., MINEHOUND, ERA Technology-Vallon GmbH) for landmine detection. The perfor- mance of the radar detector is in∞uenced by the electromagnetic characteristics of the soil. In this paper we present an experimental procedure that uses the same equipment for the detec- tion and calibration by means of signal processing procedures for the estimation of the relative permittivity of the soil. The experimental uncertainties of this method are also reported.

Time-frequency domain signature analysis of GPR data for landmine identification

In this paper, the problem of detecting buried antipersonnel (AP) landmines is tackled in the broader context of target identification: determining relevant features, extracted from impulse ground-penetrating radar (GPR) signals, which can be used to classify landmines. These features are extracted in the time-frequency domain using the Wigner-Ville distribution (WVD) and the wavelet transform (WT). Radar data are collected using the MINEHOUNDTM hand-held dual-sensor system over two types of soil and for different landmines and objects. The Wilks lambda value is used as a criterion for optimal discrimination. Results show that time-frequency signatures from WVD contain more valuable information than the features extracted using WT. Therefore, they could improve landmine and false alarm classification and help to differentiate between two different landmines.

Clutter reduction in GPR measurements for detecting shallow buried landmines: a Colombian case study

Detection of land-mines from ground-penetrating radar data is a challenging task demanding accurate and useful filtering techniques to reduce soil-surface and antenna reflections, which obscure the land-mine response. In this paper, we apply and adapt a recently proposed filtering approach to enhance the detection of shallow buried anti-personnel land-mines from data acquired in typical mine-affected soils in Colombia. The methodology combines a radar-antenna-subsurface model with phase-shift migration to filter out antenna and soil-surface effects from off-ground monostatic radar two-dimensional data. Firstly, antenna Multiple reflections are removed using linear transfer functions. Secondly, simulated Greens functions accounting for the surface reflection are subtracted. These functions are derived using the relative dielectric permittivity of the surface, which is estimated by full-wave inversion of the radar signal for measurements taken in local land-mine-free areas. Finally, the antenna radiation pattern effect is filtered out by performing phase-shift migration, and information about size and shape is extracted. Data are acquired using a hand-held vector network analyser connected to an off-ground monostatic horn antenna. Typical Colombian targets such as low-metallic anti-personnel land-mines and low- and non-metallic improvised explosive devices are used. Results prove that the proposed technique effectively reduces clutter under non-controlled conditions and yields target features that are useful for detection of these land-mines.

Closed loop hydrogeophysical inversion of time-lapse GPR data to determine hydraulic properties of a sandy soil

A closed loop hydrogeophysical inversion procedure has been developed and tested in an outdoor test site facility to investigate its effectiveness in reconstructing hydraulic properties of a sandy soil under controlled conditions. We use global optimization followed by a local routine, which involves a large number of forward models to be computed. Forward model computation speed up was established up to one order of magnitude. The final results show that the parameters describing the water content profile are well estimated, but the obtained hydraulic conductivity seems to correspond to the value excluding macropore flow.

A new integrated approach for characterizing the soil electromagnetic properties and detecting landmines using a hand-held vector network analyzer

The application of ground-penetrating radar (GPR) in humanitarian demining labors presents two major challenges: (1) the development of affordable and practical systems to detect metallic and non-metallic antipersonnel (AP) landmines under different conditions, and (2) the development of accurate soil characterization techniques to evaluate soil properties effects and determine the performance of these GPR-based systems. In this paper, we present a new integrated approach for characterizing electromagnetic (EM) properties of mine-affected soils and detecting landmines using a low cost hand-held vector network analyzer (VNA) connected to a highly directive antenna. Soil characterization is carried out using the radar-antenna-subsurface model of Lambot et al.1 and full-wave inversion of the radar signal focused in the time domain on the surface reflection. This methodology is integrated to background subtraction (BS) and migration to enhance landmine detection. Numerical and laboratory experiments are performed to show the effect of the soil EM properties on the detectability of the landmines and how the proposed approach can ameliorate the GPR performance.

Automated target recognition with SAS: Shadow and highlight-based classification

This paper presents an integrated technique for automated target recognition (ATR) in synthetic aperture sonar (SAS) images. The recognition procedure starts with a despeckling approach based on the anisotropic diffusion filter. As a second step, a fuzzymorpho-based segmentation procedure is applied to the filtered images, which partitions the image into highlights and shadow areas. A number of geometrical features are extracted from these areas, and are then used to classify targets using a Markov Chain Monte Carlo (MCMC) approach Very promising results are obtained.