NASA SBIR 2018-II Solicitation

Proposal Summary

 18-2- A3.02-4692
 Increasing Autonomy in the National Airspace Systems (NAS) (not vehicles)
 Autonomous Assessment of Airspace Operations
SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
Mosaic ATM, Inc.
540 Fort Evans Road Northeast, Suite 300
Leesburg, VA 20176
(800) 405-8576

PRINCIPAL INVESTIGATOR (Name, E-mail, Mail Address, City/State/Zip, Phone)
Michelle Eshow
540 Fort Evans Road, Suite 300
Leesburg, VA 20176 - 4098
(800) 405-8576

BUSINESS OFFICIAL (Name, E-mail, Mail Address, City/State/Zip, Phone)
Chris Stevenson
540 Fort Evans Road NE, Suite 300
Leesburg, VA 20176 - 3379
(540) 454-7458

Estimated Technology Readiness Level (TRL) :
Begin: 3
End: 4
Technical Abstract (Limit 2000 characters, approximately 200 words)

NASA’s 2018 SBIR solicitation topic A3.02 requests “Autonomous systems to produce any of the following system capabilities: Prognostics, data mining, and data discovery to identify opportunities for improvement in airspace operations.” Identifying opportunities for improvement is a critical ongoing need in the air traffic management domain, for which achieving high levels of performance is a daily concern. In Phase I, Mosaic ATM developed and delivered a machine learning model to predict terminal area (TRACON) transit times for flights arriving to Dallas/Fort Worth International Airport (DFW) based on a broad array of flight and weather input data. We also delivered a visualization capability that enables analysts to connect observed variations in TRACON transit times with their most important causes. For Phase II, we will build upon our Phase I outcomes by expanding both the sophistication of our methodologies and the performance domains we address. We will develop deep learning models of NAS performance metrics for two challenging domains: the capacity of New York area airports and the capacity of en-route airspace in the Eastern US. We will advance the state of the art in explainable deep learning for complex systems by following a rigorous process for extracting understandable basis vectors, by iteration over the internal variables of a hybrid neural network. By doing those two things, we will achieve a foundation for traffic manager decision support enhancements, to provide guidance on how to configure the Time-Based Flow Management (TBFM) system and the Traffic Flow Management System (TFMS) to maximize system-level performance. Finally, we will implement a user interface that is integrated with the NASA Sherlock ATM data warehouse, to enable NASA domain analysts to explore the models and the important features affecting performance, in a collaborative way.

Potential NASA Applications (Limit 1500 characters, approximately 150 words)
  • Expand deep learning models and user interface developed in Phase II within Sherlock data warehouse for use by NASA and partners on ATM-X and other research efforts
  • Guide future research efforts aimed at improving NAS performance; advise FAA on optimizing use of TBFM and TFMS
Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words)
  • Develop real-time tools capable of providing explainable advisories of airport and airspace capacity over a near-term time horizon for integration within an existing Mosaic ATM platform
  • Adapt the methodology and framework to run within Mosaic ATM data warehouse to enable analyses for other government agencies and commercial NAS stakeholders

Form Generated on 05/13/2019 13:31:33