Shape identification and classification in echolocation

TL;DR

This paper introduces a novel algorithm for shape identification and classification using echolocation by extracting geometric features from reflected waves and matching them with a precomputed dictionary, considering stability and noise resilience.

Contribution

It presents the first shape identification algorithm in echolocation that utilizes frequency-dependent shape descriptors based on scattering invariants.

Findings

The algorithm accurately classifies shapes in noisy conditions.

The shape descriptors are stable under measurement noise.

Numerical simulations confirm the effectiveness of the method.

Abstract

The paper aims at proposing the first shape identification and classification algorithm in echolocation. The approach is based on first extracting geometric features from the reflected waves and then matching them with precomputed ones associated with a dictionary of targets. The construction of such frequency-dependent shape descriptors is based on some important properties of the scattering coefficients and new invariants. The stability and resolution of the proposed identification algorithm with respect to measurement noise and the limited-view aspect are analytically and numerically quantified.