Small unmanned aircraft Systems (sUAS) can fly over obstacles and terrain unfit for humans, which makes them suitable for surveillance, reconnaissance and search and rescue missions. Multiple networked sUAS helps with efficient and better coverage of larger areas. Developing efficient estimation architecture for these applications is essential for the utility of the networked aircraft system. In the present work, we assess different sensor models and particle filter implementations for localization of radio frequency (RF) emitters using data received from sensors mounted on these sUAS, staying cognizant of constraints including limited computation, size, weight and power of the sensors. Performance of different particle filter implementations are evaluated and assessed based on error size and convergence rates.
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