• Wouter Van Verre

Student thesis: Phd


In 2019 there were 5,554 recorded cases of people being injured or killed by landmines and other explosive remnants of war. Landmines can remain active for a long time and kill indiscriminately, and as a result 80% of these victims are civilians. Detecting buried landmines is traditionally done using metal detectors (MD); minimum metal landmines, however, demand high levels of sensitivity. As a result the presence of metallic clutter objects in the soil causes high rates of false alarms, which can exceed 1,000 false alarms per buried landmine, which slows down the clearance operations and increases costs. Dual-modality detectors, which integrate metal detection and ground penetrating radar (GPR), could improve the false alarm rate. GPR is suitable for this use case because it can detect the larger, non-metallic, body of the landmine. The current generation of dual-modality detectors, adapted from systems developed for military purposes, have shown that the rate of false alarms can be reduced by over 90% in humanitarian contexts. This thesis describes the development of a new dual-modality detector for humanitarian demining, which uses a spectroscopic metal detector together with ultra-wideband GPR. Dual-modality detectors require close integration of the MD coils and GPR antennas, which can lead to undesirable interactions. This thesis introduces a novel bowtie antenna design which reduces the induction footprint of the antenna by 95%. The GPR antennas require a high quality feed for ultra-wideband operation, therefore multiple passive and active balun feeds were reviewed, including measurements of the common-mode currents on the feed cables. It was noted that the active baluns offer many desirable characteristics, including high levels of common-mode rejection, but they also have disadvantages, most notably the power consumption and saturation limits. Signal processing algorithms are a critical component of the dual-modality landmine detectors; a novel algorithm for the detection of metallic objects, specifically designed for detection in heavily mineralised soil, is introduced in this work. A second algorithm, based on artificial neural networks, has been designed to discriminate between metallic clutter and threat objects. Fusing the output of the MD and GPR sensors could potentially further improve the performance of the detectors. The final part of this thesis covers a novel method of measuring soil permittivity using the common-mode currents on an open-ended coaxial transmission line.
Date of Award1 Aug 2021
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorAnthony Peyton (Supervisor) & Frank Podd (Supervisor)


  • Data Fusion
  • Machine Learning
  • Magnetic Induction Spectroscopy
  • Landmine
  • Ground-Penetrating Radar
  • Metal detection
  • Ultra-Wideband

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