Predictive Data Analytics to Refine Aircrew Training and Operations

Navy STTR 21.B - Topic N21B-T024
NAVAIR - Naval Air Systems Command
Opens: May 19, 2021 - Closes: June 17, 2021 (12:00pm edt)

N21B-T024 TITLE: Predictive Data Analytics to Refine Aircrew Training and Operations

RT&L FOCUS AREA(S): Artificial Intelligence (AI)/Machine Learning (ML);Autonomy;General Warfighting Requirements (GWR)

TECHNOLOGY AREA(S): Air Platforms;Human Systems

OBJECTIVE: Research and develop a technology that supports ingesting large and disparate data sets from naval aviation aircraft and uses data science to provide outputs that increase enterprise level knowledge of aviator performance, safety, and effectiveness through data-driven predictive analytics to influence training and operations.

DESCRIPTION: The success of military operations significantly depends on the level of quality training, safety, and operational effectiveness demonstrated by its personnel. This is especially true for naval aviation operations. There are a large set of factors that affect the successful employment of naval aircraft during peacetime and wartime. These factors can change with time and with the situation and are articulated in vast and disparate data sets. These data sets, when captured, traditionally provide immediate evaluation and aircrew debrief. Generally, a vast amount of data that affects and describes crew performance is discarded or stored with no long-term data analytics processing conducted that could provide valuable trend and predictive insight.

The ability to identify performance trends is a key factor today in the effectiveness of any enterprise. This is especially true in aviation and military operations. The capability to capture large sets of performance/attribute data, and analyze the data to establish baseline and standard performance levels, enables the identification of performance anomalies, trends, and predictive outcomes. This capability has become a standard in commercial aviation and has the same applicability to military operations. The implementation of this capability to the highly complex naval aviation operations would provide great benefit from the comprehensive analysis aircrew performance to gain greater insight into areas including aircraft flight path management, procedural compliance, stores deployment, situational awareness, threat/error management, distraction management, environmental effects, aircraft envelope management, and many other performance areas. However, solutions must address both the opportunities and the challenges associated with data analytic solutions [Ref 1].

The Navy requires a technology that supports ingesting large and disparate data sets from naval aviation aircraft, supporting required parsing, sorting, and fusion to manage relevant data. Development efforts should focus on providing data analytic functionality that results in outputs that increase enterprise-level knowledge of aviator performance, safety, and effectiveness. Further, the technology functionality should extend traditional data science solutions to include capabilities for data-driven predictive analytics to influence training and operations [Ref 2]. The research and development effort should provide focus on the visualization capabilities to increase end user understanding of data analysis processes and outputs, in addition to an underlying data analytic architecture. The technology developed must meet the system DoD accreditation and certification requirements to support processing approvals for use through Risk Management Framework [Refs 4, 5, and 7] and any use of artificial intelligence (AI) as part of defined solutions should understand ethical use recommendations [Ref 6]. The policy cited in Department of Defense Instruction (DoDI) 8510.01, Risk Management Framework (RMF) for DoD Information Technology (IT) [Ref 3] and compliance with appropriate DoDI 8500.01, Cybersecurity [Ref 8] are necessary to support future transition needs.

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 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: Develop, design, and demonstrate a strategy, taking into consideration the feasibility, suitability, and acceptability, to leverage all available aircraft and related crew performance data. Identify potential roadblocks likely to be encountered and formulate approaches to overcome them. Design an architecture and implementation plan illustrating the benefits of training analytics through training use cases to demonstrate benefits of predictive analytics. The Phase I effort will include prototype plans to be developed under Phase II, with consideration for options on system architecture (e.g., Navy Marine Corps Intranet (NMCI), standalone system).

PHASE II: Develop a working prototype of the selected concept to include high-level requirements, design, initial testing, and demonstration. Demonstrate the prototype in a lab or live environment. Planning and consideration for information assurance compliance and certification for an authority to operate, including updates to support installation on Navy Marine Corps Intranet (NMCI) systems or other DoD hardware.

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

PHASE III DUAL USE APPLICATIONS: Extend the baseline functionality to include advanced or more robust data analytic techniques, and/or integrate developed capability with existing database and analysis systems. Implement Risk Management Framework guidelines [Refs 3, 4, 5, 6, and 7] to support information assurance compliance and certification for an authority to operate, including updates to support installation on NMCI systems or other DoD hardware.

Data analytics are relevant to a range of other domains such as athletics and medical communities. For medical communications, rapidly evolving situations with minimal established information is a critical and timely use case given novel infectious diseases; in addition to traditional data analytics for trends, understanding potential predictive analytics will inform decisions at various levels of leadership based on expected trends. Further, domains with quickly advancing technology due to the rapid pace of innovation and advances will benefit from similar technology solutions as a means to provide unique insights based on data analytics and predictive analyses.

REFERENCES:

  1. Fan, J., Han, Fang, and Liu, Han. "Challenges of big data analysis." National Science Review, 1(2), 2014 February 5, pp. 293–314. https://doi.org/10.1093/nsr/nwt032
  2. "Top 53 bigdata platforms and bigdata analytics software." Predictive Analytics Today, 2020. https://www.predictiveanalyticstoday.com/bigdata-platforms-bigdata-analytics-software/
  3. Takai, T.M. "DoDI 8510.01 Risk management framework (RMF) for DoD Information Technology (IT)." Department of Defense, 2012 March 12. https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodi/851001p.pdf?ver=2019-02-26-101520-300
  4. Ellett, J.M. and Khalfan, S. "The transition begins: DoD risk management framework." CHIPS, 2014 April-June. https://www.doncio.navy.mil/chips/ArticleDetails.aspx?ID=5015
  5. "Information technology. Risk management framework (RMF)." AcqNotes: Defense Acquisitions Made Easy (n. d.). http://acqnotes.com/acqnote/careerfields/risk-management-framework-rmf-dod-information-technology
  6. "AI principles: Recommendations on the ethical use of artificial intelligence by the Department of Defense." Department of Defense, Defense Innovation Board (n. d.). https://media.defense.gov/2019/Oct/31/2002204458/-1/-1/0/DIB_AI_PRINCIPLES_PRIMARY_DOCUMENT.PDF
  7. "Information Technology Laboratory. Risk Management Framework (RMF) Overview." National Institute of Standards and Technology, 2020 October 13. https://csrc.nist.gov/projects/risk-management/rmf-overview
  8. Takai, T.M. "DoDI 8500.01 Cybersecurity." Department of Defense, 2014 March 14. https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodi/850001_2014.pdf
  9. Defense Counterintelligence and Security Agency. (n.d.). https://www.dcsa.mil/Mission-Centers/Critical-Technology-Protection/NISP-Authorization-Office-NAO-/RMF/
  10. "DoD 5220.22-M National Industrial Security Program Operating Manual (Incorporating Change 2, May 18, 2016)." Department of Defense. https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodm/522022m.pdf

KEYWORDS: Qualitative analysis; data analytics; human performance assessment; data trends; statistical analysis; predictive analytics; predictive analysis

TPOC-1: Beth Atkinson

Phone: (407) 539-4356

 

TPOC-2: Mitchell Tindall 

Phone: (850) 532-0822

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