Target Identification Using Dictionary Matching of Generalized Polarization Tensors

TL;DR

This paper introduces a rapid target identification method in imaging by matching measured generalized polarization tensors to a precomputed dictionary, leveraging new invariants and shape representations for improved stability and noise resilience.

Contribution

It presents a novel, efficient shape identification algorithm using GPTs with new invariants, suitable for real-time applications and robust against measurement noise.

Findings

Algorithm achieves real-time target identification.

Numerical tests demonstrate robustness to noise.

Stability and resolution are quantitatively analyzed.

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.