Reducing the Threat to Public Safety: Improved metallic object characterisation, location and detection

Lead Research Organisation: University College London
Department Name: Mathematics

Abstract

The United Nations (UN) have announced that "With more than 30,000 foreign terrorist fighters from some 100 countries around the world, terrorism is a global threat requiring a comprehensive and unified response." This statement followed a spate of recent terrorist attacks, including those in France and Germany (July 2016), and a growing sense of global uncertainty in the western world. Promoting improvements to the identification and location of metallic threat items is an important aspect of the unified response and is an area where engineering and science can make a significant impact. Improvements in metal detection (MD) technology also provides wider benefits to the humanitarian cause of clearing landmines in developing countries. Wider benefits exist for the technology being transferred to MD companies developing devices for the non-destructive testing (NDT) of materials for safe structures, ensuring food safety, improved scrap metal sorting, as well as in medical imaging and archaeological searches.

Of course current metal detectors do find highly conductive objects and their simple design (and portability) has made them a highly cost effective modality for safety and security applications. Unfortunately, current technology is not able (or has limited capability) to distinguish between objects of different shape and materials of objects and can only detect objects within a small stand-off distance (or buried depth).

This proposal is aimed at overcoming these drawbacks through an interdisciplinary approach to improving MD technology, combing engineering, mathematics and scientific computation. Our hypothesis is that the response of metallic items in low frequency electromagnetic fields can be accurately described using a tensor based approximation. To test this hypothesis, we will develop a complete laboratory demonstration of our MD approach. This includes the following novel aspects: an efficient and adaptable software that can compute tensor coefficients for in-homogeneous objects, an algorithm for identifying different targets from field measurements with embedded uncertainty quantification as well as enhancing MD measurements by building new coil arrays based on optimised coil design. The goal is that our complete software and measurement package will lead to a step change in MD.

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