Electrosensing of inhomogeneous targets

TL;DR

This paper develops a shape classification method for inhomogeneous targets using electro-sensing data from weakly electric fish, leveraging new invariants of polarization tensors to distinguish different object types.

Contribution

It introduces novel invariants for contracted generalized polarization tensors that enable classification of inhomogeneous targets from electro-sensing measurements.

Findings

Numerical simulations demonstrate successful classification of inhomogeneous targets.

The method effectively compares invariants with a precomputed dictionary for target identification.

The approach advances shape recognition in electro-sensing applications.

Abstract

This paper addresses the electro-sensing problem for weakly electric fish in the case of inhomogeneous targets. It aims at providing a shape descriptor-based classification for inhomogeneous targets from measurements of the potentials on the skin of the fish. The approach is based on new invariants for the contracted generalized polarization tensors associated with inhomogeneous objects. The numerical simulations show that by comparing these invariants with those in a dictionary of precomputed homogeneous and inhomogeneous targets, one can successfully classify the inhomogeneous target.