A k-nearest neighbour (KNN) classification algorithm has been added to a walk-through metal detection system which is capable of inverting the magnetic polarisability tensor of metallic targets at a frequency of 10 kHz. Pre-computed library data is used to determine the class of the object, e.g. 'knife' or 'mobile phone', and is consequently capable of determining if an object is considered a threat. The results presented show a typical success rate of 95%. An investigation into classification accuracy between different candidates is also presented to determine the significance of the body effect on the success of the classification.
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