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

Article type: Research Article

Authors: Tran, M.D.J. | Lim, C.P. | Abeynayake, C. | Jain, L.C.

Affiliations: 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

Note: [] Corresponding author. E-mail: [email protected] (L.C. Jain).

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.

Keywords: Metal detector, wavelet transform, fuzzy ARTMAP neural network, majority voting, automated target discrimination

DOI: 10.3233/IFS-2010-0438

Journal: Journal of Intelligent & Fuzzy Systems, vol. 21, no. 1, 2, pp. 89-99, 2010