Multi-scale classification for electro-sensing

TL;DR

This paper presents a novel multi-scale, real-time electro-sensing classification method inspired by electric fish, utilizing shape descriptors from polarization tensors and Dempster-Shafer fusion for robust target recognition.

Contribution

It introduces a new multi-scale approach combining GPT-based shape descriptors and evidence fusion, enhancing electro-sensing target classification accuracy.

Findings

The method achieves high recognition accuracy in simulations.

It demonstrates robustness against noise and target variations.

The approach mimics biological electro-sensing behavior.

Abstract

This paper introduces premier and innovative (real-time) multi-scale method for target classification in electro-sensing. The intent is that of mimicking the behavior of the weakly electric fish, which is able to retrieve much more information about the target by approaching it. The method is based on a family of transform-invariant shape descriptors computed from generalized polarization tensors (GPTs) reconstructed at multiple scales. The evidence provided by the different descriptors at each scale is fused using Dempster-Shafer Theory. Numerical simulations show that the recognition algorithm we proposed performs undoubtedly well and yields a robust classification.