Multi-Domain Data Fusion Instructional Strategies and Methods for Pilot Training

Navy SBIR 20.2 - Topic N202-112

Naval Air Systems Command (NAVAIR) - Ms. Donna Attick [email protected]

Opens: June 3, 2020 - Closes: July 2, 2020 (12:00 pm ET)

 

 

N202-112       TITLE: Multi-Domain Data Fusion Instructional Strategies and Methods for Pilot Training

 

RT&L FOCUS AREA(S): Artificial Intelligence/ Machine Learning, General Warfighting Requirements (GWR)

TECHNOLOGY AREA(S): Air Platform, Human Systems

 

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 section 3.5 of 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: Research and develop training objectives for the multi-domain environment and instructional strategies for manned-unmanned data fusion tactical decision making. Research and develop instructional tools that support defined strategies and methods to increase operator training effectiveness and mission readiness.

 

DESCRIPTION: Operator reliance on sensor fusion is becoming more prominent as platforms increase reliance on automated technology in next generation platforms. Further, as programs look to extend platform capabilities through off-board, unmanned sensor technology and capabilities, requirements for operator synthesis of data and decision-making based on manned-unmanned collaboration will become an essential part of operations. As these technologies advance, training systems must identify appropriate instructional strategies and training methods to ensure that operators understand the implications of automated technologies. Advanced platforms and systems, including Joint Strike Fighter and Strike Planning and Execution Systems, offer unique use cases facing these challenges. Additionally, interest has been expressed by programs such as Aerial Targets and Multi-Mission Tactical Unmanned Aerial Systems, and for long-term data fusion enhancements based on future system concept of operations for future helicopter platforms.

 

This SBIR topic seeks to identify unique instructional strategies necessary for supporting manned-unmanned teaming to ensure effective and efficient operator performance. Performance is measured using both automated measures derived from available data sources and observer-based measures. Significant increases from a baseline (pre-implementation of the technology) would constitute acceptable improvement, as well as impacts to expected relevant manned-unmanned teaming factors including communication, trust, and workload. Further, an analysis of crew resource management instructional methods is necessary to identify mechanisms for extending these well-established principles to manned-unmanned teaming environments to ensure training technologies and approaches best address these future requirements. As part of this effort, development and demonstration of a software technology prototype is desired that supports training built upon the instructional strategies and methods defined. The hardware and software must meet the system DoD accreditation and certification requirements to support processing approvals for use through the policy cited in Department of Defense Instruction (DoDI) 8510.01, Risk Management Framework (RMF) for DoD Information Technology (IT) [Ref 1], and comply with appropriate DoDI 8500.01, Cybersecurity [Ref 7]. Finally, research into the effectiveness of the instructional strategies and technologies developed based on these concepts is necessary to determine feasibility prior to transition.

 

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 DoD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence and Security Agency (DCSA). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances, in order 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 IAW DoD 5220.22-M during the advanced phases of this contract.

 

PHASE I: Research and develop training objectives for the multi-domain environment and instructional strategies for manned-unmanned data fusion tactical decision making. Identify training technology to assist instructors with training and/or technologies to support instructorless training (e.g., scaffolding) that might provide beneficial uses in operational contexts for operator job aids when leveraging manned-unmanned data fusion for tactical decision making. Research and develop recommendations for automation transparency to support operator tactical decision making when leveraging manned-unmanned data fusion technology in multi-domain environments. The Phase I effort will include plans to be developed under Phase II.

 

PHASE II: Research and develop instructional tools that support defined strategies and methods to increase operator training effectiveness and mission readiness, including both technology to support instructor-led and instructorless training situations. Demonstrate operational utility of the technology for providing operator job aids and adjusting the level of transparency of automated data fusion systems to increase operator performance in manned-unmanned teaming environments. Demonstrate a prototype of the software technology that considers and adheres to Risk Management Framework guidelines to support cyber-security compliance in a lab or live environment.

 

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

 

PHASE III DUAL USE APPLICATIONS: Integrate instructional tools within a training system environment and/or transition technology via a Program Office to an operational system to provide operator job aids or enhancements to operator interfaces to increase performance. Attain Risk Management Framework certification for an authority to operate within operational/training systems.

Data fusion technologies are increasingly beneficial in the commercial sector with the influx of data analytics and advances in technology. Industries that employ commercial logistics tracking, trucking, and commercial aviation (due to the likely increase of commercial drones) may all benefit from the SBIR-developed technology solutions.

 

REFERENCES:

1. �Risk Management Framework (RMF) for DoD Information Technology (IT).� Department of Defense, Washington D.C.: Executive Services Directorate, 2014. https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodi/851001_2014.pdf

 

2. BAI Information Security Consulting & Training. (2020). BAI: Information Security RMF Resource Center. Retrieved from Risk Management Framework. https://csrc.nist.gov/projects/risk-management/risk-management-framework-(RMF)-Overview

 

3. Kaelbling, L. P., Littman, M. L. & Moore, A. W. �Reinforcement Learning: A Survey.� Journal of Artificial Intelligence Research 4, 1996, pp. 237-285. https://arxiv.org/pdf/cs/9605103.pdf

 

4. Cummings, M. L., Brzezinski, A. S. & Lee, J. D. �The Impact of Intelligent Aiding for Multiple Unmanned Aerial Vehicle Schedule Management.� IEEE Intelligent Systems: Special Issue on Interacting with Autonomy, 2007, pp. 52-59. https://dspace.mit.edu/handle/1721.1/90287

 

5. Salamon, A., Housten, D. & Drewes, P. �Increasing Situational Awareness Through the Use of UXV Teams While Reducing Operator Workload.� Semantic Scholar: Cherry Hill, 2009. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.614.2182&rep=rep1&type=pdf

 

6. Breazeal, C., Hoffman, G. & Lockerd, A. �Teaching and Working with Robots as a Collaboration.� AAMAS '04: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. pp. 1030-1037. https://dl.acm.org/doi/10.5555/1018411.1018871

 

7. �Department of Defense Instruction 8500.01, Cybersecurity.� https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodi/850001_2014.pdf

 

KEYWORDS: Training, Data Fusion, Sensor Fusion, Manned-unmanned Teaming, Instructional Strategies, Job Aids