An Electromagnetic Imaging System
for
Metallic Object Detection and
Classification
By
Abdalrahman Al-qubaa
A thesis submitted to the School of Electrical, Electronic & Computer
Engineering
in partial fulfilment of the requirements for the degree of
Doctor of Philosophy
________________________________________
Faculty of Science, Agriculture and Engineering
Newcastle University, December 2012
________________________________________
Abstract
Electromagnetic imaging currently plays a vital role in various disciplines, from
engineering to medical applications and is based upon the characteristics of
electromagnetic fields and their interaction with the properties of materials. The
detection and characterisation of metallic objects which pose a threat to safety is of
great interest in relation to public and homeland security worldwide. Inspections are
conducted under the prerequisite that is divested of all metallic objects. These
inspection conditions are problematic in terms of the disruption of the movement of
people and produce a soft target for terrorist attack. Thus, there is a need for a new
generation of detection systems and information technologies which can provide an
enhanced characterisation and discrimination capabilities.
This thesis proposes an automatic metallic object detection and classification system.
Two related topics have been addressed: to design and implement a new metallic object
detection system; and to develop an appropriate signal processing algorithm to classify
the targeted signatures. The new detection system uses an array of sensors in
conjunction with pulsed excitation. The contributions of this research can be
summarised as follows: (1) investigating the possibility of using magneto-resistance
sensors for metallic object detection; (2) evaluating the proposed system by generating a
database consisting of 12 real handguns with more than 20 objects used in daily life; (3)
extracted features from the system outcomes using four feature categories referring to
the objects’ shape, material composition, time-frequency signal analysis and transient
pulse response; and (4) applying two classification methods to classify the objects into
threats and non-threats, giving a successful classification rate of more than 92% using
the feature combination and classification framework of the new system.
The study concludes that novel magnetic field imaging system and their signal
outputs can be used to detect, identify and classify metallic objects. In comparison with
conventional induction-based walk-through metal detectors, the magneto-resistance
sensor array-based system shows great potential for object identification and
discrimination. This novel system design and signal processing achievement may be
able to produce significant improvements in automatic threat object detection and
classification applications.
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