Impact of Biomimetic Pinna Shape Variation on Clutter Echoes: A Machine Learning Approach

Abstract

Bats species navigating dense vegetation based on biosonar must obtain sensory information about their environments from “clutter echoes”, i.e., echoes that are superpositions from many unresolved reflecting facets (e.g., leaves) with unpredictable individual waveforms. Prior results suggested that pinna deformations can aid performance in sensing tasks based on deterministic echo patterns, raising the question of whether varying pinna shapes can also have functional significance for biosonar tasks performed on clutter echoes. To test this hypothesis, this work investigates whether different pinna shapes have a consistent effect on clutter echoes despite the random nature of these signals. This is accomplished using a dedicated laboratory setup that produces large amounts of uncorrelated clutter echo data by agitating artificial foliage with fans between echo recordings. Deep learning then identifies the pinna shape that receives a given clutter echo using a data-driven classification approach that learns features directly from echoes without explicit physical modeling. A ResNet-50 achieves 97.8% overall classification accuracy for the pinna shape conformations (true-positive identifications 91.67–100%), whereas a two-dimensional convolutional neural network operating on echo spectrograms still achieves 90% accuracy. These findings demonstrate that even small pinna deformations can impart consistent effects on the clutter echoes.