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2014-09-22
Threat Target Classification Using ANN and SVM Based on a New Sensor Array System
By
Abdalrahman R. Al-Qubaa,

    Dr. Abdalrahman R. Al-Qubaa

    Remote Sensing Center

    Mosul University

    Iraq

    Homepage

Abeer Al-Shiha,

    Dr. Abeer Al-Shiha

    Remote Sensing Center

    Mosul University

    Iraq

    Homepage

Gui Yun Tian

    Dr. Gui Yun Tian

    School of Electrical and Electronic Engineering

    Newcastle University

    UK

    Homepage

Progress In Electromagnetics Research B, Vol. 61, 69-85, 2014
doi:10.2528/PIERB14050704 | Google Scholar

Abstract
Electromagnetic imaging is based upon the fundamentals of electromagnetic (EM) fields and their relationship with the material properties under evaluation. A new system based on a Giant Magneto-Resistive (GMR) sensor array was built to capture the scattered EM signal returned by metallic objects. This paper evaluates the new system's capabilities through the classification of metallic objects based on features extracted from their response to EM fields. A novel amplitude variation feature as well as the combinations of typical features is proposed to obtain high classification rates. The selected features of metallic objects are then applied to well-known supervisedclassifiers (ANN and SVM) to detect and classify `threat' items. A collection of handguns with other commonly used metallic objects are tested. Promising results show that a high classification rate is achieved using the proposed new combination features and classification framework. This novel procedure has the potential to produce significant improvements in automatic weapon detection and classification.
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Citation
Abdalrahman R. Al-Qubaa, Abeer Al-Shiha, and Gui Yun Tian, "Threat Target Classification Using ANN and SVM Based on a New Sensor Array System," Progress In Electromagnetics Research B, Vol. 61, 69-85, 2014.
doi:10.2528/PIERB14050704
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