SBIR/STTR Research Award

Selecting Days for Concept and Technology Evaluation in SMART-NAS Test-Bed Scenario Generation

Award identifier 7227f0478562c17a5748b9d184a6dbc7f33096b6a727e485b30c93a326a68ea0Awarding agency National Aeronautics and Space Administration

Award amount

$124,884

Program and phase

SBIR · Phase I

Award year

2017

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Award details

Awarding agency
National Aeronautics and Space Administration
Branch
Not reported
Recipient company
CROWN CONSULTING, INC.
Proposal award date
2017-06-09
Contract end date
2017-12-08
UEI
JN3UKDP675C4
Solicitation number
Not reported
Topic code
A3.02

Research abstract

Crown Consulting, Inc. will investigate and demonstrate methods to enable rapid selection of days for scenario generation in the development and evaluation of Air Traffic Management concepts and technologies (C&T) in the Shadow Mode Assessment using Realistic Technologies for the National Airspace System (SMART-NAS) Test-bed (SNTB). The proposed capability will enable the rapid generation of highly operationally relevant scenarios for use in the development and evaluation of technology demonstrators such as NASA Airspace Technology Demonstrator, ATD-2 and ATD-3, Unmanned Aerial System Traffic Management, as well as new operational concepts such as Integrated Demand Management and Trajectory Based Operations. A significant motivation for the development of the SNTB is enabling C&T benefit, impact, safety and cost assessments for speeding up deployment in the NAS. Today, C&T introduction into the NAS takes decades. The primary reason for this is an inability to assess the operational impact of the interaction between the proposed C&T and operationally deployed systems in terms of NAS-wide safety, traffic flow efficiency, roles and workload of controllers and traffic managers, and impact on airline operations. Human-In-The-Loop testing and shadow-mode evaluation driven by operational data. Slow and incremental steps are typically taken towards deployment because of limitations in the development of mathematical modeling and simulation. The proposed innovation seeks to augment the scenario generation capability of NASA's SNTB with methods and tools for selecting traffic, winds and weather based on the needs of the experiment allowing for highly operationally relevant scenarios. These methods and tools would actively categorize incoming and historical data using advanced machine-learning algorithms, allowing fast access to NAS streaming and legacy data in a big-data warehouse through queries generated via a simple user interface for specifying desired characteristics.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

11 SBIR/STTR records share this UEI (2015–2023). Reported award amounts total $4,029,976. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: National Aeronautics and Space Administration, Department of Defense

Agency intelligence

National Aeronautics and Space Administration

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