Feature extraction and classification of metal detector signals using the wavelet transform and the fuzzy ARTMAP neural network

Authors:
M. D. J. Tran;C. P. Lim;C. Abeynayake;L. C. Jain
Affiliations:
Knowledge-Based Intelligent Engineering Systems (KES) Centre, School of Electrical and Information Engineering, University of South Australia, Adelaide, SA 5095, Australia;Knowledge-Based Intelligent Engineering Systems (KES) Centre, School of Electrical and Information Engineering, University of South Australia, Adelaide, SA 5095, Australia;Threat Mitigation Group, Weapon Systems Division, Defence Science and Technology Organisation (DSTO), Edinburgh, Australia;(Correspd. E-mail: lakhmi.jain@unisa.edu.au) Knowledge-Based Intelligent Engineering Systems (KES) Centre, School of Electrical and Information Engineering, University of South Australia, Adelaide ...
Venue:
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Year:
2010

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Abstract

In this paper, the Fuzzy ARTMAP (FAM) neural network is used to classify metal detector signals into different categories for automated target discrimination. Feature extraction of the metal detector signals is conducted using a wavelet transform technique. The FAM neural network is then employed to classify the extracted features into different target groups. A series of experiments using individual FAM networks and a voting FAM network is conducted. Promising classification accuracy rates are obtained from using individual and voting FAM networks, respectively. The experimental outcomes positively demonstrate the effectiveness of the generated features, and of the FAM network in classifying metal detector signals for automated target discrimination tasks.