Spectrum Localization for Improved Situational Awareness

There is a demand for scalable, wideband architectures that support spectrum monitoring, source localization, and emitter classification. RF spectrum is becoming increasingly congested as the number of emitters grows and their bandwidths widen. Technologies that improve the DoDs ability to create an accurate, detailed assessment of operating spectrum are critical to improving situational awareness and maintaining communication capabilities in evolving conditions. In response to this need, our team is proposing a scalable architecture capable of emitter detection, localization, and classification codenamed WATCHDOGS (Wireless Awareness through Transmitter Classification by Heterogenous, Distributed Observers that Geolocate Signal Sources). During the proposed Phase I effort, the architecture and its underlying subsystems and algorithms will be implemented in a simulation environment and analyzed to quantify their performance as the complex tradespace associated with their parameters is analyzed. As part of the Phase I effort, elements of the WATCHDOGS architecture (including a classification engine) will be implemented and demonstrated on MaXentrics multi-channel adaptive digital beamforming platform to highlight functionality and reduce risk associated with future development. The WATCHDOGS architecture will provide a mechanism for keeping users better informed of the changing spectral conditions and emitters around them.