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Based upon bench and field testing five distinct sensors as candidates for use in a large area ground sensor network, our team has determined cooperative sensor fusion modes of operation for infrasound, audible acoustic, fence vibration, visible cameras, and seismic geophones. The goal has been efficient coverage of large areas to provide alerts for poaching activity hot spots so UAVs (Unoccupied Aerial Vehicles) can provide rapid response to prevent poaching without need for constant patrolling. Prior work by our research team has focused on evaluation of sensing modes with a range of spatial, temporal, and spectral capabilities (satellite, aerial, ground acoustic, electrooptical/infrared, seismic, and fence vibration). Focus has been construction of low-cost sensors for sensor fusion to provide situational awareness via web interfaces. These systems jointly developed by Embry-Riddle Aeronautical University with California State University Chico have been tested at the Chico State University farm, and top-down sensor fusion methods such as deep learning (to detect or classify animals and threats) as well as bottom-up image and signal processing have been developed to create a fog and edge computing architecture. Modalities that specifically target elephant communication with infrasound and seismic activity are being investigated to enhance overall animal detection, tracking, and assessment of behavior. The goal is to evaluate effectiveness prior to testing on-site at a game park in South Africa, and to determine if the methods can be scaled to areas as large as Rietvlei, Medikwe, and Coleridge South Africa. Preliminary results from fog and edge node testing of visual and acoustic sensor fusion with artificial emulation of elephant vocalizations, infrasound rumbles, and stimulation typical of human presence (vehicles and voices) are provided along with promise to drive a heat map showing where park rangers should respond with highest priority.
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