Intelligent Data Understanding for Architecture Analysis of Entry, Descent, and Landing

Metadata Updated: July 17, 2020

Because Entry, Descent and Landing (EDL) system validations are limited in Earth environments, these technologies rely heavily on models and analysis tools to evaluate system performance and capture uncertainties, which determine the success of a mission. The proposed research seeks to develop technologies that will provide top-level analysis capabilities for Entry, Descent, and Landing Architecture Analysis. The goal of this research is to advance the state of the art for offline Intelligent Data Understanding (IDU) technologies by incorporating an intelligent assistant that helps identify and analyze a complex data set and mine for interesting features and insight. These goals will be achieved by using adaptive operator selection algorithms to solve hard computational problems. This goal will also be met by exploring the explanation abilities of intelligent agents through visual and verbal interactions and provide critique. Secondly, this research will make use of machine learning techniques to incorporate knowledge into objective functions. These algorithms will be validated on a set of missions such as human landings on Mars and Europa. For each case study, extensive simulations will be run and sensitivity analysis and data mining will be performed to identify sensible factors that affect dependent variables during EDL. Nevertheless, the proposed research will develop the next generation of IDU technologies and will develop capabilities for high-fidelity architecture analysis to evaluate EDL choices. This technology will address NASA's challenges for developing effective computational mechanisms to identify high value data, analyze, and communicate critical issues regarding the mission. Furthermore, these technologies could also be adapted to other aspects of a mission such as mixed-initiative landing of human spacecraft, mixed-initiative exploration of planetary bodies, and multi-spacecraft collaborative on-board event detection.

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Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Created Date August 1, 2018
Metadata Updated Date July 17, 2020

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date July 17, 2020
Publisher Space Technology Mission Directorate
Unique Identifier TECHPORT_94151
Public Access Level public
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id 5138f734-1609-4da1-870a-4f9759aa187e
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2019-08-01
Homepage URL
Data Last Modified 2020-01-29
Program Code 026:027
Source Datajson Identifier True
Source Hash 86cfc15a8c44e3915e8587fdabb6f22bfbbfd106
Source Schema Version 1.1

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