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Spectrum Localization for Improved Situational Awareness



OBJECTIVE: Develop a scalable multi-channel, multi-band architecture and algorithms capable of supporting Spectrum localization for improved situational awareness. 

DESCRIPTION: Spectrum monitoring and RF source geolocation are critical tools for maintaining a situational awareness advantage in rapidly changing RF conditions. Modern urban battlefields are rich in RF emitters (friendly, hostile, and neutral) that are progressively wider band and operating across a growing range of frequencies. In response to this increasingly difficult challenge, the AFRL is seeking scalable, multi-channel architectures and supporting algorithms capable of collecting and digitizing multiple wideband antenna frontends, localizing RF emitters, classifying them, and efficiently presenting the information to the warfighter. These functions can be used to help the warfighter in scenarios that include: countering interference and jamming, monitoring RF emissions associated with suspicious activity, coordinating RF emissions among friendly users, and making jamming operations much more effective. To meet the needs of future Spectrum Localization missions, it is critical that the proposed platform limit the assumptions regarding waveforms, and sampling rates to the absolute minimum. Instead, the platform should be specified and designed from the point of view of data throughput. In addition, platform flexibility should be ensured via either flexible expansion modules or firmware upgrades. Currently, given current state-of-the-art systems, each processing element should be able to process data at a rate of >500 Gbps. In addition, at a minimum, the platform should be able to support 16 channels of with an instantaneous bandwidth greater than 1GHz. Similarly, the processing elements should be able to simultaneously handle both high input data rates and high-complexity algorithms for signal classification. 

PHASE I: Develop source localization and classification algorithms and design a blueprint for a scalable architecture that supports these algorithms. Investigate SWaP and performance tradeoffs. Tradeoffs include instantaneous bandwidth, tunable bandwidth, dynamic range, number of beams / (bandwidth of beams), number of identifiable waveforms/features and system scalability. 

PHASE II: Develop, demonstrate, and a multi-channel prototype platform capable of supporting at least 16 channels with an instantaneous bandwidth greater than 1GHz. 

PHASE III: Develop spectral monitoring hardware and software for transition to appropriate platforms. 


1. T. S. Rappaport, Smart Antennas: Adaptive Arrays, Algorithms, and Wireless Position Location, IEEE Press, 1998.

2. V. Kalinichev, "Analysis of beam-steering and directive characteristics of adaptive arrays for mobile communications", IEEE Antennas Propagation Magazine, Vol. 43, No. 3, pp. 145-152, 2001.


KEYWORDS: Spectrum Localization, Direction Finding, Beamforming, Classification, Scalable Processing 

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