NASA SBIR 2014 Solicitation


PROPOSAL NUMBER: 14-1 A20.01-8672
SUBTOPIC TITLE: Air Traffic Management Research and Development
PROPOSAL TITLE: Large-Scale Data Analysis Using Machine Learning Framework for Trajectory Prediction Algorithms

SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
Optimal Synthesis, Inc.
95 First Street, Suite 240
Los Altos, CA 94022 - 2777
(650) 559-8585

PRINCIPAL INVESTIGATOR/PROJECT MANAGER (Name, E-mail, Mail Address, City/State/Zip, Phone)
Veera V. V. S. Vaddi
95 First Street, Suite 240
Los Altos, CA 94022 - 2777
(650) 559-8585 Extension :105

CORPORATE/BUSINESS OFFICIAL (Name, E-mail, Mail Address, City/State/Zip, Phone)
Victor Cheng
95 First Street, Suite 240
Los Altos, CA 94022 - 2777
(650) 559-8585

Estimated Technology Readiness Level (TRL) at beginning and end of contract:
Begin: 1
End: 3

Technology Available (TAV) Subtopics
Air Traffic Management Research and Development is a Technology Available (TAV) subtopic that includes NASA Intellectual Property (IP). Do you plan to use the NASA IP under the award?

TECHNICAL ABSTRACT (Limit 2000 characters, approximately 200 words)
A significant portion of the NextGen research is aimed at (i) developing ground-side automation systems to assist controllers in strategic planning operations such as scheduling flights, and (ii) developing tactical controller decision support tools to separate and space the traffic. Central to the success of these automation systems is the ability to predict the future trajectory of any aircraft in the National Airspace System (NAS). The research related to this area is referred to as Trajectory Prediction (TP) and sometimes Trajectory Synthesis. Notwithstanding past research, TP remains a very challenging exercise and the quest for improved TP accuracy continues. Any improvements in TP can benefit a wide array of NextGen concepts pursued by NASA. The objective of the current research is to seek a novel approach to TP specifically aimed at addressing some of the deficiencies of the past TP research. The approach involves: (i) machine learning algorithms, and (ii) big data computational platforms.

Phase I research will demonstrate the benefits of supervised and unsupervised machine learning algorithms for TP. Phase II research seeks to develop real-time trajectory prediction algorithms that can be used for a wide variety of NASA NextGen concepts.

POTENTIAL NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
Algorithms developed under the current research are expected to directly contribute towards NASA's NextGen air traffic management research, especially to the Separation Assurance (SA) research focus area.

POTENTIAL NON-NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
The TP algorithms developed under this research are expected to be applicable all over the National Airspace System. These algorithms could be part of the En Route Automation Modernization (ERAM) currently being developed by Lockheed Martin for the FAA. During Phase II research, Optimal Synthesis Inc. seeks to identify transition mechanisms for implementing these algorithms in ERAM software system.

TECHNOLOGY TAXONOMY MAPPING (NASA's technology taxonomy has been developed by the SBIR-STTR program to disseminate awareness of proposed and awarded R/R&D in the agency. It is a listing of over 100 technologies, sorted into broad categories, of interest to NASA.)
Air Transportation & Safety
Algorithms/Control Software & Systems (see also Autonomous Systems)
Data Modeling (see also Testing & Evaluation)
Data Processing

Form Generated on 04-23-14 17:37