Planar Array Capacitive Imaging for Landmine Detection

  • Carl Chittenden

Student thesis: Doctoral ThesisPhD


Landmines are still a very existent threat to large parts of the world and some reports even suggest that it is increasing. A method which could aid in their discovery is Electrical Capacitance Tomography (ECT) which can perform non-invasive sub-surface imaging. ECT can create permittivity distributions and using a planar array sensor should be possible to distinguish artificial landmines form their natural surroundings. A software toolbox, DeTECT , was developed at the start of the project made specifically for landmine detection using ECT. New planar array sensors were designed which improved the depth at which ECT could reconstruct inclusions. From this work a new sensor, the Combined Sensor, was developed which showed how further improvements in object shape and location detection could be made. In order to improve image reconstruction
a new Automatic Parameter Selection (APS) algorithm was created which
solved the problem of manually selecting the best parameters and will make using ECT for landmine detection more accessible. The Combined Sensor was implemented by designing and manufacturing a bespoke switching mechanism, the Impedance Analyser (IA) Switch, which allowed further improvements in permittivity distribution imaging. Finally, scanning methods were conceived and tested which will have a direct application to landmine detection in the future. They showed that using temporal reconstruction could significantly improve the shape and location detection of objects both in simulations and in real experimental setups. These methods were then tested on a large scale to show how by scanning a sensor over an area, a complete image of the entire region could be reconstructed using a special technique which performs complete reconstructions with the aid of multiple measurements. Using the new Combined Sensor in conjunction with scanning techniques and APS could compliment existing landmine technology and help to reduce risk to future detectorists.
Date of Award22 Nov 2018
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorManuchehr Soleimani (Supervisor)

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