Spectral descriptors and supervised classifier for ammonium nitrate detection in landmines by nuclear quadrupole resonance.

Abstract

The high specificity of Nuclear Quadrupole Resonance (NQR) makes it very suited for the detection of antipersonnel mines, where the intensity of the signal spectrum around the resonance frequency of the target substance is the standard decision parameter; however, radiofrequency interference, soil effects on the search coil, landmine size, burial depth, and target temperature affect signal intensity. To overcome this, the use of spectral descriptors and a supervised classifier are proposed in this work, where an assembly of decision trees was trained with NQR data collected on places where a target filled with ammonium nitrate was present and where it was not. A statistical test, comparing the proposed classifier and the solution based solely on the intensity of the signal spectrum, showed with significant evidence that the proposed classifier outperforms the traditional solution. A final blind experiment was conducted in a rural region of Colombia, where five landmines of different size filled with ammonium nitrate were shallowly buried in an area of 1.9 × 1.52 m, and the system with the proposed classifier detected four of them with three false alarms. This work is also novel in detecting ammonium nitrate in antipersonnel mines, which are typical in Colombia, the second most mined country in the world.