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SV-Training and Kernel Change Detection Algorithm for the Abrupt Modification in EMI Data for Buried Metallic Target Localization and Identification

Abstract : 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 dierent depths, the amplitude of the deeper buried object is negligible compared to that of the other object. This would result in the fact that, the EMI system can not detect 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 dierent depths are in the same range. The data resulting from this space are easier to process. To validate the proposed method, we have tested it on real EMI data derived from a series of measurements on real objects.
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https://hal.archives-ouvertes.fr/hal-01132608
Contributor : Saïd Moussaoui Connect in order to contact the contributor
Submitted on : Tuesday, March 17, 2015 - 3:43:53 PM
Last modification on : Wednesday, April 27, 2022 - 4:55:26 AM

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  • HAL Id : hal-01132608, version 1

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Yacine Matriche, Saïd Moussaoui, Mouloud Feliachi, Abdelhalim Zaoui, Mohammed Abdellah. SV-Training and Kernel Change Detection Algorithm for the Abrupt Modification in EMI Data for Buried Metallic Target Localization and Identification. Applied Computational Electromagnetics Society Journal (ACES Journal), 2015, 30 (1), pp.132-139. ⟨hal-01132608⟩

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