Optimization of sensor deployment for acoustic detection and localization in terrestrial environments

Abstract

The rapid evolution in miniaturization, power efficiency and affordability of acoustic sensors, combined with new innovations in smart capability, are vastly expanding opportunities in ground-level monitoring for wildlife conservation at a regional scale using massive sensor grids. Optimal placement of environmental sensors and probabilistic localization of sources have previously been considered only in theory, and not tested for terrestrial acoustic sensors. Conservation applications conventionally model detection as a function of distance. We developed probabilistic algorithms for near-optimal placement of sensors, and for localization of the sound source as a function of spatial variation in sound pressure. We employed a principled-GIS tool for mapping soundscapes to test the methods on a tropical-forest case study using gunshot sensors. On hilly terrain, near-optimal placement halved the required number of sensors compared to a square grid. A test deployment of acoustic devices matched the predicted success in detecting gunshots, and traced them to their local area. The methods are applicable to a broad range of target sounds. They require only an empirical estimate of sound-detection probability in response to noise level, and a soundscape simulated from a topographic habitat map. These methods allow conservation biologists to plan cost-effective deployments for measuring target sounds, and to evaluate the impacts of sub-optimal sensor placements imposed by access or cost constraints, or multipurpose uses.

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