Clutter reduction techniques for GPR based buried landmine detection

Landmines are affecting the lives and livelihoods of millions of people around the globe. There are more than 110 million landmines in nearly 62 countries cause over 26,000 casualties each year. The costs of single landmine for buying and planting are about 3-10$ whereas it costs 300-1000$ for removal. Annually, about 80,000 APMs were being lifted, while about 2.5 million new APMs were being planted. Modern landmines are mainly non-metallic or contain small metal, so that conventional metal detectors cannot be used efficiently. Considerable efforts are put in GPR development for detection of shallow buried landmines. But, GPR also performs inadequately due to clutter, which dominates the data and obscures the main information. Clutter varies with surface roughness and soil conditions lead to measurement uncertainty and high false alarm rates. In this paper, advanced signal processing techniques are going to be used to reduce the clutter and we will compare background reduction techniques based on their test statistic. It is seen the width of the window has an impact on the estimation. It is found that running median of an appropriate window size is the best in reducing background clutter, running mean is the next.