DON26BZ05-NV074 TITLE: Automated Post-Mission De-Brief and Re-Planning for Collaborative Combat Aircraft (CCA) Missions

OUSW (R&E) CRITICAL TECHNOLOGY AREA(S): Applied Artificial Intelligence (AAI)

COMPONENT TECHNOLOGY PRIORITY AREA(S): Advanced Computing and Software

PROJECTED CMMC LEVEL REQUIREMENT: Level 2 (Self)

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. 

OBJECTIVE: Develop automated capabilities for post-mission debriefs that enable rapid re-planning for future missions. By leveraging advanced analytics, diagnostics, and algorithms, this program aims to enhance mission effectiveness and adaptability of Collaborative Combat Aircraft (CCA) operations in contested environments by utilizing advanced analytics, diagnostics, and algorithms.

DESCRIPTION: Modern naval operations require the ability to adapt swiftly to evolving threats, especially during multi-day missions involving CCA. Current post-mission analysis is resource-intensive and often too slow to provide timely, actionable insights for subsequent engagements. This program aims to automate the post-mission debrief and re-planning process for CCA missions to enable faster, more informed decision-making.

The primary objective is to develop automated capabilities for post-mission debriefing that facilitate rapid re-planning for subsequent missions. By leveraging advanced analytics, diagnostics, and algorithms, this program seeks to enhance the mission effectiveness and adaptability of CCA operations in contested environments. The solution must deliver actionable insights from mission data to inform re-planning decisions, optimizing tactics and operational parameters in real-time for the next engagement.

The solution will concentrate on developing algorithms and tools to analyze mission data, identify key performance metrics, characterize uncertainty, and generate explainable recommendations for subsequent mission planning. The main areas of emphasis include:

• Blue Force Optimization: Refining the operational parameters, autonomy behaviors, algorithms, task allocation, routing, tactics, and employment concepts of collaborative combat aircraft based on mission outcomes. Recommendations should identify expected mission benefits, survivability implications, operational constraints, and confidence in the projected result.

• Red Force Modeling: Improving mission planning and execution through analysis of adversary tactics, capabilities, threat profiles, observed behaviors, and likely future course of action. The solution should identify uncertainty in Red Force assessments and provide alternative threat interpretations where the available evidence does not support a single high-confidence conclusion.

• Autonomous Re-Planning: Use machine learning, data analytics, optimization and decision-support methods to generate and compare potential follow-on mission plans based on real world outcomes and lessons learned. The solution should recommend multiple feasible courses of action, identify associated confidence levels and operational tradeoffs, and provide traceable rationale to support operator review and approval.

This effort will also seek to answer the following critical questions:

• What data points are most crucial for effective post-mission analysis and re-planning?

• How can systems be designed to enable rapid, iterative updates to mission plans?

• What mechanisms are most effective for selecting and adapting algorithms in real-time based on the available data?

By tackling these challenges, the program aims to create a framework that streamlines post-mission analysis and enables rapid, intelligent re-planning for CCA missions. This will ultimately enhance the effectiveness and survivability of naval assets in high-stakes environments.

Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by 32 U.S.C. § 2004.20 et seq., National Industrial Security Program Executive Agent and Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence and Security Agency (DCSA) formerly Defense Security Service (DSS). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances. This will allow contractor personnel to perform on advanced phases of this project as set forth by DCSA and NAVAIR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material during the advanced phases of this contract IAW the National Industrial Security Program Operating Manual (NISPOM), which can be found at Title 32, Part 2004.20 of the Code of Federal Regulations.

PHASE I: Outline the strategy for tackling the post-mission debriefing and re-planning challenges unique to CCA missions. This process involves choosing suitable artificial intelligence (AI) or machine learning algorithms, supported by a comprehensive review of existing literature and prior research.

The Phase I effort will include prototype plans to be developed under Phase II.

PHASE II: Develop the automated debrief and re-planning tools. Performers will implement their selected approach within a Navy-approved simulation environment, where they will demonstrate its effectiveness using multi-day mission simulations. The resulting solution must integrate seamlessly with existing CCA systems and include clear documentation for operational use.

Work in Phase II may become classified. Please see note in Description paragraph.

PHASE III DUAL USE APPLICATIONS: Transition the technology into operational systems supporting CCA and autonomous mission systems. It will be integrated with mission planning environments and autonomy framework to support the fleet.

Since industry leverages AI-driven solutions, it could easily adopt similar frameworks to improve commercial systems in the private/civilian sectors.

REFERENCES:

1. Kelleher, John D., Brian Mac Namee, and Aoife D'arcy. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies. MIT Press, 2020.
2. Rizk, Yara, Mariette Awad, and Edward W. Tunstel. "Cooperative Heterogeneous Multi-Robot Systems: A Survey." ACM Computing Surveys (CSUR) 52.2 (2019): 1-31.
3. Moubayed, Abdallah, et al. "E-Learning: Challenges and Research Opportunities Using Machine Learning & Data Analytics." IEEE Access 6 (2018): 39117-39138.
4. Kibria, Mirza Golam, et al. "Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks." IEEE Access 6 (2018): 32328-32338.
5. Kashyap, Hirak, et al. "Big Data Analytics in Bioinformatics: A Machine Learning Perspective." arXiv preprint arXiv:1506.05101 (2015).
6. Alighanbari, Mehdi, and Jonathan P. How. "Decentralized task assignment for unmanned aerial vehicles." Proceedings of the 44th IEEE Conference on Decision and Control. IEEE, 2005.
7. National Industrial Security Program Executive Agent and Operating Manual (NISP), 32 U.S.C. § 2004.20 et seq. 1993". https://www.ecfr.gov/current/title-32/subtitle-B/chapter-XX/part-2004

KEYWORDS: CCA; Autonomous Mission Planning; Post Mission; Blue/Red Force Modeling; AI Decision Support; Multi-Agent Autonomy

TPOC 1 : Johann Soto
(347) 924-1047
johann.e.soto.civ@us.navy.mil

TPOC 2 : Mark Gallagher
(760) 939-1267
mark.w.gallagher6.civ@us.navy.mil