# Applied Multi-Domain Sense Making

**R-2A activity** of project 626095 — Sensor Fusion Technology  
**Program element:** 0602204F — Aerospace Sensors  
**Component:** U.S. Air Force · **Budget Activity:** 2  
**Vintage:** President's Budget PB2027  
**Canonical URL:** https://hitchintel.com/programs/0602204F/626095/a0  
**Parent:** https://hitchintel.com/programs/0602204F/626095

## Summary

This activity requests $20.0M in FY2027, 58% of project 626095, down 29% on FY2026. The R-2A exhibit describes it across FY2026–FY2027, including what the FY2027 money is planned to buy.

## What the FY2027 request buys

**FY2027 planned work.** - 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

**FY2026 to FY2027 change.** 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.

## Before the request year

**FY2026 plans — current year.** - 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.

## Funding

| Fiscal Year | Estimate Type | Amount ($M) |
|---|---|---|
| FY2025 | Actual | 8.6 |
| FY2026 | Enacted | 28.4 |
| FY2027 | Request | 20.0 |

> Prior, current and budget year only — an R-2A activity carries no five-year plan. It sums exactly into its project in the request year and not necessarily in any other.

## Other activities in project 626095

- [Sensing Autonomy](https://hitchintel.com/programs/0602204F/626095/a2) — FY2027 14.6
- Multi-Domain Sensing Effects and Analysis — FY2027 0.0
- Cyber Physical Sensing — FY2027 0.0

## Source & machine access

- **Source:** FY2027 Department of the Air Force RDT&E Budget Justification, Exhibit R-2A, PE 0602204F project 626095 (PB PB2027). Narrative is the government's own text.
- **No marks, no contractors at this grain** — congressional marks land on the program element and R-3 performers on the project.
- **MCP:** `mcp.hitchintel.com` — `budget_get_activity`.

*HitchAI is an independent intelligence service, not affiliated with the U.S. Department of Defense. Budget figures are requests/estimates, not obligations.*