Aerial Drones with Location-Sensitive Ears

Abstract

Micro aerial vehicles (MAVs), an emerging class of aerial drones, are fast turning into a high value mobile sensing platform for applications across sectors ranging from industrial to humanitarian. While MAVs have a large sensory gamut at their disposal; vision continues to dominate the external sensing scene, which has limited usability in scenarios that offer non-visual clues such as auditory. Therefore, we endeavor to provision a MAV auditory system (i.e., ears); and as part of this goal, our preliminary aim is to develop a robust acoustic localization system for detecting sound sources in the physical space of interest. However, devising this capability is extremely challenging due to strong ego-noise from the MAV propeller units, which is both wideband and non-stationary. It is well known that beamformers with large sensor arrays can overcome high noise levels; but in an attempt to cater to the platform (i.e., space, payload, and computation) constraints of a MAV, we propose DroneEARS: a binaural sensing system for geo-locating sound sources. It combines the benefits of sparse (two elements) sensor array design (for meeting the platform constraints), and our proposed mobility induced beamforming based on intra-band and inter-measurement beam fusion (for overcoming the severe ego-noise and its other complex characteristics) to significantly enhance the received signal-to-noise ratio (SNR). We demonstrate the efficacy of DroneEARS, in terms of SNR improvement obtained over many widely used techniques, by empirical evaluations.