SV-Training and Kernel Change Detection Algorithm for the Abrupt Modification in EMI Data for Buried Metallic Target Localization and Identification
Keywords:
Decision index, electromagnetic induction, kernel change detection algorithm, mine detection, support vector trainingAbstract
In this paper, we propose a new method to identify and to locate buried metallic object in ElectroMagnetic Induction (EMI) data based on the Kernel Change Detection (KCD) algorithm. The signature of the object in the EMI data is typically of low amplitude. Particularly, in the case where two objects are located at different depths, the amplitude of the deeper buried object is negligible compared to that of the object buried in the first centimeter of the soil. This would result in the fact that, the EMI system can neglect the signature of this object and consequently increases miss rate or False Negative Rate (FNR). The aim of the proposed method is to calculate a decision index for each EMI measurement in a so-called hypotheses space using KCD algorithm. The amplitude of these decision indexes in the case of objects at different depths are in the same range, which make their variance smaller. Indeed, this index will be compared to a threshold for judging the presence or absence of a rupture. The validation of the proposed method is performed by processing real EMI data derived from a series of measurements on real objects.
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