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Repurposing Computational Analyses of Tactics for Training Assessments
Navy STTR 2018.A - Topic N18A-T003 NAVAIR - Ms. Donna Attick - [email protected] Opens: January 8, 2018 - Closes: February 7, 2018 (8:00 PM ET)
TECHNOLOGY
AREA(S): Air Platform, Human Systems, Information Systems ACQUISITION
PROGRAM: PMA-276 H-1 USMC Light/Attack Helicopters OBJECTIVE:
Design and develop a software technology that leverages data science and
advanced computational analyses of tactical data sources to improve training
scenarios and assessments and make training more adaptive, efficient, and
effective. DESCRIPTION:
Emerging warfare capabilities offer a great many new tactical options to
commanders.� However, this also increases the demands on decision-makers during
operations.� The dynamic and complex nature of integrated warfare results in
training challenges to prepare for those engagements.� As the complexity of
Tactics, Techniques, and Procedures (TTPs) increase, testing in part via
computational simulation and optimization is necessary.� Such analyses
systematically vary tactical applications of the warfare capability to a
variety of threat scenarios, simulate and score each encounter, and generate a
ranked list of the most successful tactics per threat.� The scenarios,
measures, and knowledge generated in this type of work are rich and voluminous,
providing opportunities to leverage data science. PHASE
I: Design methods and determine the feasibility of a software that can
repurpose the output of data analyses (e.g., mission analysis, TTP analysis,
modeling and simulation testing, aircraft system data logs) to generate
recommendations for tactical training scenarios and assessments in complex
warfare capabilities.� Demonstrate the feasibility of data science approaches
for use in a software technology solution.� Risk Management Framework
guidelines should be considered and adhered to during the development to
support information assurance compliance.� In preparation for human subjects�
experiments in Phase II, research protocols and Institutional Review Board
(IRB) applications should be developed and submitted.� The Phase I effort will include
prototype plans to be developed under Phase II. PHASE
II: Develop prototype software technology that leverages data science
approaches to repurpose the output of data analyses to support tactical
training scenario and assessment (i.e., performance measures) generation in
complex warfare capabilities.� Conduct human factors analyses to ensure the
usability of the prototype software.� Conduct human subjects� experiments that
validate training effects and benefits of auto-generated scenario and performance
measurement outputs of the software technology for a single-use case.� Risk
Management Framework guidelines should be considered and adhered to during the
development to support information assurance compliance. PHASE
III DUAL USE APPLICATIONS: Expand the development of the software technology to
additional use cases and aviation platforms.� Demonstrate the reliability and
validity of system outputs for effective tactical training scenario and
assessment (i.e., performance measures) generation in complex warfare
capabilities.� Complete the process to seek a standalone Authority To Operate
(ATO) and/or support a transition training site to incorporate the developed
training solution into an existing ATO depending on transition customer�s
desire.� Conduct test and integration activities with target transition data
analysis outputs and training system inputs.� Improvements in technology to
repurpose data analysis outputs is applicable to all military and commercial
systems where system generated logs (e.g., commercial aviation) are collected.�
Further, technology developed in this STTR topic would be applicable to most
military systems where data is output in one stage of the acquisition process
(e.g., modeling and simulation testing) to increase re-use for reduction of
resources and/or schedule in later stages.� In the training environment, this
type of technology also provides an opportunity to increase the effectiveness
and fidelity of training scenarios while increasing instructional capabilities
through relevant performance assessment tools. REFERENCES: 1.
Kitchin, R. "Big Data, new epistemologies and paradigm shifts." Big
Data & Society, April-June 2014, I-12. http://bds.sagepub.com/content/1/1/2053951714528481.full.pdf+html 2.
Fan, J., Han, F., and Liu, H. "Challenges of Big Data Analysis."
(Published 05 February 2014) National Science Review, Volume 1, Issue 2, 1 June
2014, Pages 293-314. http://nsr.oxfordjournals.org/content/1/2/293.short 3.
"Top 50 Big Data Platforms and Big Data Analytics Software." Data
Science Platform. http://www.predictiveanalyticstoday.com/bigdata-platforms-bigdata-analytics-software/#content-anchor 4.
Labrinidis, A. and Jagadish, H. V. "Challenges and Opportunities with Big
Data." Journal Proceedings of the VLDB Endowment, Vol. 5, Issue 12, August
2012, pp 2032-2033. 5.
Risk Management Framework (RMF) for DoD Information Technology (IT)F. www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodi/851001_2014.pdf 6.
Risk Management Framework: https://rmf.org/
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