Optimal forward regression for landmine detection by thermal sensing

In this paper, starting from the GOFR algorithm, a new Forward Regression algorithm for landmine detection and localization using thermal methods is presented. The efficiency of such algorithm is described by showing a valid representation of the typical temperature waveforms taken after heating the ground surface, and detection of temperature anomalies due to the presence of hidden objects. Optimizations to the algorithm are then showed, with the aim of a significant sampling density reduction in space and time.