What the FY2027 request buys
Verbatim from the R-2A exhibit for project 625315 of PE 0602788F. 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 research and development of tactical platforms in concert with space-based simulation software to develop a comprehensive sensing grid orchestrator to optimize orchestration of available tactical and space resources for extended custody of targets in highly contested environments. Activities include development of a strategic sensing grid for single modality space-based tracking.
FY 2027 funding decreased compared to FY 2026 by $10.195 million due to re-prioritization to meet the nation's future security needs and the completion of research and development for querying and exploitation of knowledge graphs with Large Language Models, for automatic target recognition leveraging topologically informed neural networks, for an automated multi-source data fusion and spatio-temporal grounding capability, multi-channel emitter detection and geolocation system hardware, intercept processing software, and automated target detection and recognition software, advancing Strategic Sensing Grid orchestration and data exploitation for Situational Awareness (SA), advancing the application of novel neuromorphic systems for robust and dynamic machine learning, for a High Value Target (HVT) recommendation system to seek correlations between non-traditional data source signatures and multi-satellite actions, and to produce a supercomputer in a compact container for AI/ML computing.
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.
- Complete research and development of an automated multi-source data fusion and spatio-temporal grounding capability, seeking opportunities for future applications with existing targeting systems to reduce the investigative burden of warfighter analysts through automation of the targeting research and prosecution process. Activities include testing and assessing developed algorithms to demonstrate increased efficiency for analysis and targeting. - Complete research and development of multi-channel emitter detection and geolocation system hardware, intercept processing software, distributed sensing and automated target detection, recognition software, and ontology development to enable persistent intelligence, surveillance, & reconnaissance execution on unmanned platforms in theaters of high interest where traditional manned platforms have limited ability. Activities include testing efficient emitter detection algorithms/models using edge computing systems. - Complete research to advance the Strategic Sensing Grid orchestration and data exploitation to provide a collaborative, autonomous, Sensing Grid (SG) for Situational Awareness (SA) of ground and air high value targets (HVTs). Activities include creating multi-target scenarios to demonstrate performance benefits of autonomous detection tracking and identification. - Complete advancing the application of novel neuromorphic systems for robust and dynamic machine learning, including on mobile or power-constrained platforms to enable extreme computing at the sensor to accelerate the Find, Fix, Track, & Target process for the warfighter. Activities include testing and assessing developed algorithms using operationally relevant datasets. - Complete research and development for a HVT recommendation system to optimize moving target indicator (MTI) trackers to best ensure HVTs are monitored and non HVTs are disregarded. - Complete research to seek correlations between non-traditional data source signatures and multi-satellite actions to enhance the ability to predict multi-satellite engagement scenarios and assess threat impacts. - Complete research and development to collaborate with designated universities to produce a supercomputer in a compact container to enhance Artificial Intelligence/Machine Learning (AI/ML) computing at the sensor for real-time data processing for improved decision making. - Commence and complete research and development for querying and exploitation of knowledge graphs (KGs) with Large Language Models (LLMs) to reduce the investigative burden on warfighter analysts, enabling faster and more accurate decision-making. - Commence and complete research and development to develop and experiment on automatic target recognition (ATR) leveraging topologically informed neural networks (TINN), allowing the warfighter to overcome adversarial deceptions (e.g., jamming). - Commence research and development of tactical platforms in concert with space-based simulation software to develop a comprehensive sensing grid orchestrator to optimize orchestration of available tactical and space resources for extended custody of targets in highly contested environments. Activities include development of a strategic sensing grid for single modality space-based tracking.
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 | 0.0 |
| FY2026 | Enacted | 39.6 |
| FY2027 | Request | 29.4 |
This activity is 31% of project 625315's FY2027 request and 20% of PE 0602788F'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.
6 activities in project 625315
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.