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On November 1, the Air Force Research Laboratory (AFRL) posted updates to the Adaptive Fusion, Analysis & Reasoning of multi-source data (AFAR) broad agency announcement (BAA). For best funding consideration in FY25, white papers are due by July 15, 2024.
AFRL’s Information Directorate (AFRL/RI), Information Fusion Branch, is soliciting white papers under this announcement for unique and innovative technologies to explore and develop AFAR capabilities including but not limited to, analytics, analytical tools, algorithm developments, projects, and experiments that will provide the Air Force the means to better conduct analytical operations in support of their Command, Control, Communications, Computers and Intelligence (C4I) and Cyber Science mission. This announcement is comprised of three research areas: (A) Adaptive Knowledge and Information for Target Analysis (AKITA), (B) Enhancing Moving Target Engagement at Scale (EMoTES) and (C) Publicly Available Information (PAI) Ensemble Fusion (PEF), where each has research areas that taken together comprise the focus of AFAR research and development.
Air Force analysts are inundated with large quantities of data that need to be cultivated and assessed throughout the peer fight challenge. Due to the quantity and diversity of AF Intelligence data, this analysis can be time consuming and costly, as it remains a manually intensive task for analysts. Fundamentally the analysis of data must produce higher levels of comprehension and understanding than presently exists. As data and information have increased in both quantity and complexity, the demands for greater analytical capabilities have also grown dramatically. According to the Next Generation ISR Dominance Flight Plan (2018 – 2028), the Air Force does not fully harness or integrate all potential information sources at its disposal, resulting in limited insights into adversary intent, capability, and operational execution. The ability of decision makers to receive, process, and react to actionable intelligence is inhibited by the dispersion of information across platforms and networks. Further development is required to not just keep pace but to move beyond current performance levels, to overcome limitations in moving to new data types and domains, and to achieve new, more sophisticated capabilities.
One of the greatest technical challenges facing all decision support systems is the heterogeneous aspect of the data that is collected by millions of sensors and the different stovepipe architectures used to store this data. In order to perform useful analytics, a composite picture of the key entities, events, and locations need to be pieced together from the original disparate data sources. The ingesting, fusion and understanding of information from disparate data sources remains a difficult and unresolved problem.
AFRL is seeking novel research and development approaches to advancing the automation of comprehensive knowledge graph generation and enhancing moving target engagements at scale. This BAA also seeks the development of state-of-the-art technologies in data alignment for large-scale, disparate data sources and creating analytical models for estimating “tactical” phenomena that occur in publicly available information (PAI)/commercially available information (CAI).
Source: SAM
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