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… Click to show full 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.
               
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