Automated Framework for Target Enrichment and Activity Modeling (AFTEAM)

It is vital that commanders and planners have as complete of understanding of the battlespace as possible to base decisions on. As the Air Force implements the doctrine set forth by Agile Combat Employment (ACE), airmen will be required to work proactivity within threat timelines to help achieve that understanding. This will require analysts to translate Command and Control (C2) and Intelligence information into action at the speed and scale of relevance, regardless of the operational environment. To accomplish this, designers of Intelligence and Analytics systems are presented with a tradeoff between offering more data sources or offering a more streamlined workflow for the analyst. The option chosen nearly always seems to fall on the side of more data sources. If the probability of finding an “intelligence golden nugget” is proportional to the amount and diversity of data ingested, the design reasoning goes, then more data and more sources of data increase the probability of finding something important. JDM Solutions understands this perspective on intelligence analysis, but also acknowledges that a more-is-better approach doesn’t necessarily lead to more insightful or timely analysis. Some analysis problems are matters of finding a “nugget” in a sea of data and connecting the dots. Analysts are often tasked with a Sherlock Holmes style job of finding patterns of behavior for a potential target or patterns between multiple potential targets. In these cases, the quantity of data ingested as a measure of a system’s potential analytic power is secondary to the measure of the quantity of connections that can be made across disparate elements of data. Our vision for an Automated Framework for Target Enrichment and Activity Modelling (AFTEAM) will move from simple situational awareness to true understanding. The AFTEAM’s Contextual Reporting Engine (CRE) which will offer a holistic overview of integrated sensor and reporting data, serving as a vital bridge between raw sensor data and reported activities. Users will benefit from a unique perspective on tracked events and activities, enabling analysis of historical trajectories and comprehension of specific occurrences in time and space. The CRE facilitates this through both a user interface for operators and an API for machine-to-machine interaction, supporting efficient search and query functionalities based on object type, location, and time/duration. The versatility extends to data products, presented in a human-readable format, contributing to more informed analysis and decision-making regarding tracked entities and their associated events.