Target Identification Using Dictionary Matching of Generalized Polarization Tensors
Habib Ammari, Thomas Boulier, Josselin Garnier, Wenjia Jing, Hyœnbæ Kang, Han Wang
Abstract
The aim of this paper is to provide a fast and efficient procedure for (real-time) target identification in imaging based on matching on a dictionary of precomputed generalized polarization tensors (GPTs). The approach is based on some important properties of the GPTs and new invariants. A new shape representation is given and numerically tested in the presence of measurement noise. The stability and resolution of the proposed identification algorithm is numerically quantified.