Landmine detection by means of ground penetrating radar: a model-based approach

The presence of landmines poses a worldwide humanitarian problem. Often, these mines are difficult to detect with metal detectors. Ground penetrating radar (GPR) is a promising technology for the detection of landmines with low metal content. Automatic landmine detection typically consists of two steps, namely preprocessing (or clutter removal) and classification. In this paper the clutter removal algorithm consists of a nonlinear frequency domain filter followed by principal component based filtering. Principal component analysis is performed in the frequency domain to build a background model for the clutter. The latter model is removed from the observed data in the log-frequency domain in order to preserve the phase component of the spectrum. Finally, the data is normalized and transformed to the time domain. The results presented in this paper show a marked improvement in the ability to remove general background clutter. Classification is performed on the basis of the prediction performance of neural network time-series models of the various classes of GPR responses. The classification system can correctly identify the position of metal anti-tank (AT) mines. It can also recognize specific examples of low metal AT and (anti-personnel) AP mines, but does have a low generalization ability for such mines