Detection and classification from electromagnetic induction data

TL;DR

This paper presents a novel, efficient algorithm for identifying conductive objects from electromagnetic induction data, leveraging geometric features, invariants, and a new shape identification scheme, with demonstrated stability and resolution in noisy conditions.

Contribution

The paper introduces a new shape identification method based on electromagnetic induction data, using invariants and polarization tensors, with comprehensive numerical validation.

Findings

Algorithm effectively identifies objects with noisy data

High stability and resolution demonstrated in simulations

Utilizes new invariants for shape matching

Abstract

In this paper we introduce an efficient algorithm for identifying conductive objects using induction data derived from eddy currents. Our method consists of first extracting geometric features from the induction data and then matching them to precomputed data for known objects from a given dictionary. The matching step relies on fundamental properties of conductive polarization tensors and new invariants introduced in this paper. A new shape identification scheme is introduced and studied. We test it numerically in the presence of measurement noise. Stability and resolution capabilities of the proposed identification algorithm are quantified in numerical simulations.