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.

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.

We propose an adaptive plastic-landmine visualizing radar system employing a complex-valued self-organizing map (CSOM) dealing with a feature vector that focuses on variance of spatial- and frequency-domain inner products (V-CSOM) in combination with aperture synthesis. The dimension of the new feature vector is greatly reduced in comparison with that of our previous texture feature-vector CSOM (T-CSOM). In experiments, we first examine the effect of aperture synthesis on the complex-amplitude texture in space and frequency domains. We also compare the calculation cost and the visualization performance of V- and T-CSOMs. Then we discuss merits and drawbacks of the two types of CSOMs with/without the aperture synthesis in the adaptive plastic-landmine visualization task. The V-CSOM with aperture synthesis is found promising to realize a useful plastic-landmine detection system.

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.

Matching Pursuits (MP), a technique for signal decomposition using a dictionary of functions, is applied to ground penetrating radar (GPR) signals in order to remove noise and clutter included in the signals and to extract target responses. A wave-based dictionary composed of wavefronts and resonances is employed. Noise reduction performance and the removal of ground-surface reflection are evaluated through numerical simulations. The results show that the MP approach performs well and offers an effective method for feature extraction from GPR signals.

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.