Biomimetic Mappings for Active Sonar Object Recognition in Clutter

Abstract

SONAR technology plays a pivotal role in terrain exploration and specifically identification of objects of interest. However, it grapples with a recurring challenge of clutter and noise which limits the performance of target recognition models. The challenge of noisy observations renders the choice of robust signal representations critical. Inspired by mammalian representation in the midbrain of echolocating bats, the present study evaluates the robustness of a decomposition of echo measurements that matches the statistics of natural vocalizations. This representation is contrasted with equally rich generic mappings as well as digital sonar images based on time-frequency representations. The study shows the clear advantage of the naturally optimized representation for object recognition in presence of background noise and clutter, and further underscores the potential of bio-inspired approaches in advancing SONAR technology.