Accurate Target Localization by using Artificial Pinnae of brown long-eared bat

TL;DR

This paper presents an artificial bat ear device that accurately estimates target elevation using echolocation and neural networks, mimicking the natural abilities of brown long-eared bats.

Contribution

The study introduces a novel artificial pinnae design and a neural network-based method for precise elevation estimation, advancing bio-inspired target localization technology.

Findings

Higher accuracy in target elevation estimation.

Binaural shape and orientation are crucial for localization.

Effective neural network model with cross-validation.

Abstract

Echolocating bats locate the targets by echolocation. Many theoretical frameworks have been suggested the abilities of bats are related to the shapes of bats ears, but few artificial bat-like ears have been made to mimic the abilities, the difficulty of which lies in the determination of the elevation angle of the target. In this study, we present a device with artificial bat pinnae modeling by the ears of brown long-eared bat (Plecotus auritus) which can accurately estimate the elevation angle of the aerial target by virtue of active sonar. An artificial neural-network with the labeled data obtained from echoes as the trained and tested data is used and optimized by a tenfold cross-validation technique. A decision method we named sliding window averaging algorithm is designed for getting the estimation results of elevation. At last, a right-angle pinnae construction is designed for determining direction of the target. The results show a higher accuracy for the direction determination of the single target. The results also demonstrate that for the Plecotus auritus bat, not only the binaural shapes, but the binaural relative orientations also play important roles in the target localization.