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UAV Tracking System for Monitoring Wildlife
Principal Investigator
Michael Shafer
Collaborators
Paul Flikkema and Carol Chambers (Northern Arizona University)
Overview
The aim of this project is to construct an unmanned aerial system that integrates a radio telemetry receiver and data processing system for efficient detection and localization of tiny wildlife radio telemetry tags. Current methods of locating and tracking small tagged animals are hampered by the inaccessibility of their habitats. The high costs, risk to human safety, and small sample sizes resulting from current radio telemetry methods limit our understanding of the movement and behaviors of many species. UAV-based technologies promise to revolutionize a range of ecological field study paradigms due to the ability of a sensing platform to fly in close proximity to rough terrain at very low cost. The new UAV-based radio telemetry (UAV-RT) system will dramatically improve wildlife tracking capability while using inexpensive, commercially available radio tags. The system design will reflect the unique needs of wildlife tracking applications, including easy assembly and repair in the field and reduction of fire risk.
Our effort will focus on the development, analysis, real-time implementation, and test of software-defined signal processing algorithms for RF signal detection and localization using unmanned aerial platforms. It combines (1) aspects of both wireless communication and radar signal processing, as well as modern statistical methods for model+data inference of RF source locations, and (2) fusion-based inference that draws on RF data from both mobile UAV-based receivers and ground-based systems operated by expert humans.
The complete design, including parts, assembly plans, software, and training modules for the UAV-RT system will be open sourced for widespread adoption of the technology, with dissemination and outreach by a user community website and demonstrations at wildlife conferences.
Funding
Funded by the National Science Foundation (NSF) through IDBR award #1556417.