AFRL posts BAA for Object Based Production (OBP) and Activities Based Intelligence (ABI) tradecraft

On December 8, the Air Force Research Laboratory posted the following broad agency announcement for Object Based Production and Activities Based Intelligence Technology Development for Indications and Warning (BAA NUMBER: BAA AFRL-RIK-2016-0001). For best consideration in FY16, the agency recommends that interested parties submit white papers by March 1, 2016.

The focus of this broad agency announcement (BAA) is to research, develop, demonstrate, integrate and test innovative technologies for Object Based Production (OBP) and Activities Based Intelligence (ABI) tradecraft in support of multi-domain Automated Indications and Warnings (AI&W).

This BAA seeks capabilities for the discovery and analysis of emerging activities across multiple domains (ground, air, maritime, space and cyberspace) in order to provide tactical and strategic level indications and warning and to reduce the time needed to search and correlate data from multiple sources on multiple systems. Methods are needed to:

– Provide richer and more robust patterns of life to support timely, effective and efficient command and control decisions
– Characterize, locate and compare/contrast activities, driving rapid data exploration and discovery of significant events
– Anticipate events by building a deep understanding of the networks that give rise to specific incidents

This announcement seeks research and development which demonstrates the ability to synergistically apply OBP and ABI technologies. The specific technology areas of interest include but are not limited to:

– Algorithms for exploiting data to learn normal patterns of behavior, detect deviations from normalcy, and anticipate future behavior
– Learning algorithms which construct models of normal activity patterns at a variety of conceptual, spatial, and temporal levels to reduce a massive amount of data to a rich set of information regarding the current status of active models
– Algorithms which discover gaps in existing knowledge, create hypotheses about what the missing data might provide, and provide a recommended collection tasking
– Continuous incremental learning which enables the models of normal behavior to adapt well to evolving situations while maintaining high levels of performance.
– Algorithms for uncovering tactics, techniques and procedures (TTP) to impact ongoing operations
– Mining relevant data from overwhelmingly big data
– Cloud based data and information sharing
– OBP optimized processing and auto-association

Full information is available here.

Source: FedBizOpps