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Enhanced Path Planning, Guidance, and Estimation Algorithms for NASA's GMAT

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNX12CE16P
Agency Tracking Number: 114492
Amount: $124,996.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: O4.03
Solicitation Number: N/A
Timeline
Solicitation Year: 2011
Award Year: 2012
Award Start Date (Proposal Award Date): 2012-02-13
Award End Date (Contract End Date): 2012-08-13
Small Business Information
1235 South Clark Street Suite 400
Arlington, VA -
United States
DUNS: 036593457
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Belinda Marchand
 Principal Investigator
 (703) 414-5001
 belinda.marchand@dac.us
Business Contact
 April Lesho
Title: Business Official
Phone: (703) 414-5004
Email: april.lesho@dac.us
Research Institution
 Stub
Abstract

Advanced trajectory design and estimation capabilities in complex nonlinear dynamical regimes represent two of the greatest technical challenges of modern space flight. In addressing these challenges, DECISIVE ANALYTICS Corporation seeks to advance the capabilities of NASA's open source General Mission Analysis Tool to integrate the latest advances in trajectory path planning and estimation. This includes the development of an Advanced Path Planning (APP) plugin that leverages concepts from dynamical systems theory, multi-phase targeting, and visualization for trajectory design in regions where multi-body effects are significant, such as near the libration points. Parallel to that is the development of an Advanced Estimation (AE) plugin, which leverages the results of past studies done at DECISIVE analytics for the Missile Defense Agency and the US Air Force. The proposed AE plugin will be designed around a Hybrid Dynamic Bayesian Network framework, pioneered by DECISIVE ANALYTICS, which will enable advanced estimation capabilities including Unscented Kalman Filters and Gaussian Mixture Models. These two techniques, particularly Gaussian Mixture Models, offer enhanced predictive capabilities for the determination of the true probability density when nonlinearities significantly influence the estimation process. Phase 1 will focus on the software development, integration, testing, and validation of initial prototypes for both plugins.

* Information listed above is at the time of submission. *

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