What the FY2027 request buys
Verbatim from the R-2A exhibit for project 626095 of PE 0602204F. This is the budget justification's own description of work that has not happened yet — the one thing no other level of the budget carries.
- Continue a system-of-systems construct utilizing the latest in DevSecOps, Government Reference Architectures, and Open Mission Standards to generate capability at the tactical edge, focusing on integrating an algorithm pipeline and reducing the time to transition from applied research to advanced demonstrations. - Continue to generate knowledge by fusing information from multiple spatial and temporal sensing systems, improving the state of the art in multi-domain sensemaking, focusing on associating tracks with high confidence identification for moving targets. - Continue research, development, and application of machine reasoning techniques and next-generation information understanding tools to identify intent/purpose of stationary and moving objects of interest in multiple domains, over a broad set of sensing operating conditions, focusing on the knowledge representation and fusion of relevant external contextual information with collected sensing data. - Continue development of performance evaluation techniques addressing both single-intelligence sensing systems as well as closed-loop systems-of-systems, focusing on expanding proof-of-concept results that utilize advanced artificial intelligence (e.g., deep learning foundation models), multi-modal modeling, synthetic data, and evaluation science to accurately predict target recognition algorithm performance in a wide range of mission scenarios. - Continue to perform empirical performance measurements in addition to performance prediction estimates, focusing on automated/autonomous intelligence, surveillance, and reconnaissance exploitation systems of military-critical targets with limited train and test data. - Complete research in new novel techniques to exploit unforeseen information from non-traditional information sources. - Commence research and development of detection, tracking, and identification for new and uncommon targets, leveraging cluster compute, cloud compute, and high-performance compute facilities, focusing on the rapid generation of synthetic training data to train reliable and predictable algorithms and models that leverage artificial intelligence and machine learning. - Commence use of advanced Artificial Intelligence, multi-modal modeling, synthetic data, and evaluation science to accurately predict target recognition algorithm performance in a wide range of mission scenarios
FY 2027 decreased compared to FY 2026 by $8.314 million due to a reduction in the development of enhancement and expansion of air target Combat ID technologies. This reduction is the result of a strategic realignment of the Department of the Air Force's Science and Technology (DAF S&T) portfolio, which aims to optimize core research areas and improve resource efficiency.
FY2026: the year under way
Prior-year accomplishments and current-year plans from the same exhibit. Context for the FY2027 plan, not a series — an activity partitions its project exactly in the request year, but can under-cover it in earlier years.
- Continue a system of systems construct utilizing the latest in development, security, and operations and open mission standards to generate capability at the tactical edge. - Continue to generate knowledge by fusing information from multiple spatial and temporal sensing systems, improving the state of the art in information fusion. - Continue to provide solutions in contested, train/test data limited environments. - Continue to advance state of the art algorithm techniques leveraging artificial intelligence with deep learning and machine learning. - Continue research, development, and application of machine reasoning techniques and next-generation information understanding tools to identify intent/purpose of stationary and moving objects of interest in multiple domains, over a broad set of sensing operating conditions. - Continue to advance research in multi-domain sense making applied to air, ground, and maritime surface targets. - Continue to improve the amount of time required to move research from basic to applied to advanced demonstrations. - Continue to standardize integration environments, expand simulation capabilities, and investigate model-based systems engineering best practices. - Continue development of performance evaluation techniques addressing both single-intelligence sensing systems as well as closed-loop systems-of-systems. - Continue to perform empirical performance measurements in addition to performance prediction estimates for automated/autonomous intelligence, surveillance, and reconnaissance exploitation systems of military-critical targets with limited train and test data. - Continue to leverage cluster compute, cloud compute, and high-performance compute facilities. - Continue research in new novel techniques to exploit unforeseen information from non-traditional information sources.
Three years, and no five-year plan
An R-2A activity publishes the prior year, the current year and the budget year. The FYDP outyears exist at project and program-element level and are deliberately absent here rather than inferred. Estimate types are colored and never summed into one figure.
| Fiscal Year | Estimate Type | Amount ($M) |
|---|---|---|
| FY2025 | Actual | 8.6 |
| FY2026 | Enacted | 28.4 |
| FY2027 | Request | 20.0 |
This activity is 58% of project 626095's FY2027 request and 12% of PE 0602204F's. In the request year the activities under a project sum to it exactly; in the current year they under-cover it in about 9% of cases, so an activity's delta can legitimately exceed its parent's and the two must not be compared row to row.
4 activities in project 626095
Every R-2A line of this project, largest FY2027 request first. Linked where the activity has enough of its own narrative to carry a page; the rest are shown in full on the project page.