The EC DELVE Support Action project has analyzed the bottlenecks in the transfer of Humanitarian Demining (HD)
technology from technology development to the use in the field, and drawn some lessons learned, basing itself on the
assessment of the European Humanitarian Demining Research and Technology Development (RTD) situation from early
1990 until 2006. The situation at the European level was analyzed with emphasis on activities sponsored by the
European Commission (EC). This was also done for four European countries and Japan, with emphasis on national
activities. The developments in HD during the last 10 years underline the fact that in a number of cases demining related
developments have been terminated or at least put on hold.
The study also showed that the funding provided by the EC under the Framework Program for RTD has led directly to
the creation of an extensive portfolio of Humanitarian Demining technology development projects. The latter provided a
range of research and supporting measures addressing the critical issues identified as a result of the regulatory policies
developed in the field of Humanitarian Demining over the last ten years. However, the range of instruments available to
the EC to finance the necessary research and development were limited, to pre-competitive research. The EC had no
tools or programs to directly fund actual product development. As a first consequence, the EC funding program for
development of technology for Humanitarian Demining unfortunately proved to be largely unsuitable for the small-scale
development needed in a field where there is only a very limited market. As a second consequence, most of the research
has been demonstrator-oriented. Moreover, the timeframe for RTD in Humanitarian Demining has not been sufficiently
synchronized with the timeframe of the EC policies and regulations. The separation of the Mine Action and RTD
funding streams in the EC did also negatively affect the take-up of new technologies.
As a conclusion, creating coherence between: (1) the EC policy based on political decisions, (2) RTD, testing and
industrialization of equipment, and (3) timely deployment, requires a new way of coordinated thinking: "end-to-end
planning" has to be supported by a well organized and coordinated organizational structure involving different DGs and
even extending beyond the EU. This was not the case for Mine Action, but appears today to be the case for
Environmental Risk Management.
In order to extract accurate quantitative information out of Ground Penetrating Radar (GPR) measurement data, one needs to solve a nonlinear inverse problem. In this paper we reformulate this into a nonlinear least squares problem which is non convex. Solving a non-convex optimization problem requires a good initial estimation of the optimal solution. In this paper we use a three step method to solve the just described non-convex problem. In a first step the qualitative solution of the linearized problem is found to obtain the detection and support of the subsurface scatterers. For this first step Synthetic Aperture Radar (SAR) and MUltiple SIgnal Classification (MUSIC) are proposed and compared. The second step consists out of a qualitative solution of the linearized problem to obtain a first guess for the material parameter values of the detected objects. The method proposed for this is Algebraic Reconstruction Theorem (ART), which is an iterative method, starting from the initial value, given by the first step, and improving on this until an optimum is achieved. The final step then consists out of the solution of the nonlinear inverse problem using a variational method. The paper starts with a discussion of the GPR inverse problem and continues with a short description of the used methods (SAR, MUSIC, ART and adjoint method). Finally an example is given based on simulated data and some conclusions are drawn.
This work deals with the processing of GPR (ground penetrating radar) signals for AP (anti-personnel) mine detection. It focuses on two steps in this processing, namely the deconvolution of the system impulse response, and the extraction of target features for classification. The objective of the work is to find discriminant and robust target features by means of time-frequency analysis. Deconvolution is an ill-posed inverse problem, which can be solved with regularization methods. In this paper a deconvolution algorithm, based on the iterative v-method, is proposed. For discriminant feature selection the Wigner distribution (WD) is considered. Singular value decomposition (SVD) along with the concept of the center of mass as the most robust feature are used for feature extraction from the WD. The proposed normalized time-frequency-energetic features have a good discriminant power, which doesn't degrade with increasing object depth.
This paper describes the theory and practice of ground penetrating radar (GPR) clutter characterization and removal. Clutter and target parametric and non-parametric modeling methods are described and results of these methods on laboratory data are presented. Data were collected at the Technische Universitaet Ilmenau using a 6 GHZ frequency- stepped GPR. Targets were chosen to include rocks and other non-lethal clutter which normally present false targets to the GPR. Results indicate a quantifiable improvement in target class discrimination using the clutter reduction methods over standard mean background removal methods.
One of the main problems with the interpretation of GPR data is the strong ground reflection, obscuring signals arriving from just underneath the surface. The strength of this reflection can be reduced by deconvolution. This technique is especially useful when GPR is used to detect buried landmines. Parametric and non parametric time variant estimators are used for clutter characterization. Wavelet decomposition/reconstruction for noise removal is then applied. The application of the proposed signal processing technique to GPR data yields a substantial enhancement of the target reflection as well as a good estimation of physical parameters such as propagation velocity and target position.