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Cognitive Fully Autonomous Sensor Technology (CFAST)

Description:

TECHNOLOGY AREA(S): Sensors 

OBJECTIVE: Develop next generation cognitive and fully autonomous sensor technologies amenable to a highly distributed airborne swarm 

DESCRIPTION: The global proliferation of advanced integrated defense system technologies continues to present a major challenge. It is recognized across the DoD that a distributed swarm platform architecture is needed. Major advances in commercial technologies i.e. embedded computing with integrated neuromorphic engine, software defined radios, and radar system on a chip, there is a major challenge in putting all this together. The goal of this research is to develop CFAST amenable technologies. Emphasis is on solutions that are low size, weight, power and cost. All physical sensor modalities will be considered. The focus of this research is the sensor payload, packaging, and platform integration, not on communications or networking. 

PHASE I: Develop a fully integrated conceptual design for CFAST technologies. A basic feasibility analysis using modeling and simulation to establish a new sensor approach. 

PHASE II: Further refine and prototype the CFAST design. The output should be a design that is ready to enter into a Phase III 

PHASE III: DUAL USE APPLICATION: The proposer will identify potential commercial and dual use applications such as low SWAP-C sensors and embedded computing that include cognition engines. 

REFERENCES: 

1. L. O. Spencer, "US Air Force Key to Third Offset Strategy," in Defense News, ed, 2016.; 2. Airborne Swarms. Available: https://www.darpa.mil/news-events/2017-04-23; 3. Small form factor embedded computing. Available: https://en.wikichip.org/wiki/apple/ax/a11; 4. R. Guerci and E. J. Baranoski, "Knowledge-aided adaptive radar at DARPA: an overview," Signal Processing Magazine, IEEE, vol. 23, pp. 41-50, 2006.
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