Abstract
Landmines pose a significant problem in many countries around the world. Although technological systems such as metal detectors have been employed to combat these threats, many of these still require significant human interaction especially in the area of target and clutter discrimination. The aim of this research is to develop an automated decision making system for landmine detection. The initial stages of the research involves comparing various techniques for feature extraction to determine which methods provide the best representation for metal detector data to achieve improved target discrimination from background noise. This paper will focus on evaluating a technique utilizing the Continuous Wavelet Transform with false alarm rate and probability of detection used as performance measures.
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Tran, M.DJ., Abeynayake, C. (2009). Evaluation of the Continuous Wavelet Transform for Feature Extraction of Metal Detector Signals in Automated Target Detection. In: Nakamatsu, K., Phillips-Wren, G., Jain, L.C., Howlett, R.J. (eds) New Advances in Intelligent Decision Technologies. Studies in Computational Intelligence, vol 199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00909-9_24
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DOI: https://doi.org/10.1007/978-3-642-00909-9_24
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