NASA Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) New Observing Strategies (NOS)
No abstract available
Le Moigne, Jacqueline↗
Engineering topics
Le Moigne, Jacqueline
Publications and source records attributed to Le Moigne, Jacqueline.
New Observing Strategy (NOS) for Future NASA Earth Science Missions
No abstract available
Le Moigne, Jacqueline↗
Distributed Spacecraft Missions (DSM)
This talk presents the concept of "Distributed Spacecraft Mission (DSM)", its drivers, its challenges and its benefits to NASA future Science and Exploration missions. The presentation focuses on the technology challenges related to DSMs and particularly the needs for Trusted Autonomy and Onboard Processing; particular attention is given to a specific type of DSM, the Intelligent and Collaborative Constellation (ICC). Examples of DSM, ICC and Autonomy-related technologies being currently developed at NASA are being introduced.
Le Moigne, Jacqueline↗
New Observing Strategy (NOS) for Future Earth Science Missions
One of the new thrusts of the Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) Program is the New Observing Strategy (NOS) thrust. Its goal is to provide a framework for identifying technology advances needed to exploit newly available observational capabilities, particularly to enable the development of the information technologies needed to support planning, evaluating, implementing, and operating dynamic, multi-element sets of observing assets. In this paper, we will introduce relevant NOS terminology and some key concepts before describing the objectives, driving factors and technology goals of this new thrust.
Advanced Information Systems↗
Testbed Requirements to Enable New Observing Strategies
Emerging capabilities to integrate instruments on smallsats, airborne platforms and in situ devices into an intelligent, distributed observing strategy show great promise for measuring Earth science natural phenomena and physical processes that have not previously been characterized. To reduce the threshold for success in deploying such an intelligent, integrated observing strategy, a ground-based testbed system is proposed. Virtually all of the technologies needed for using such a tool have matured to the point of being used, individually. Virtually none of the technologies have been deployed, working together. The technologies to be deployed should be integrated into a working "breadboard" where the components can be debugged and performance and behavior characterized and tuned-up. A system of this complexity should not be expected to work without full integration and experimental characterization. Further, and perhaps more importantly, in order to successfully propose a space-based element to this strategy, teams must convince the relevant science community that the risk is low enough to warrant the investment. The main benefit of the testbed is to retire the risk of integrating these new technologies and increase the Technology Readiness Level (TRL) of each component as well as the System Readiness Level (SRL) of the integrated system.
Little, Mike↗
Earth Science Technology Office (ESTO) New Observing Strategies (NOS) and NOS-Testbed (NOS-T)
No abstract available
Le Moigne, Jacqueline↗
Introduction to NASA Goddard Workshop on Artificial Intelligence
Artificial Intelligence (AI) is a collection of advanced technologies that allows machines to think and act, both humanly and rationally, through sensing, comprehending, acting and learning. AI's foundations lie at the intersection of several traditional fields Philosophy, Mathematics, Economics, Neuroscience, Psychology and Computer Science. Although the inception of AI started in the 1950's, it has recently made a strong comeback in all aspects of society and all over the world; this is mainly due to the timely combination of increased data volumes, advanced and mature algorithms, and improvements in computing power and storage. Current AI applications include big data analytics, robotics, intelligent sensing, assisted decision making, and speech recognition just to name a few.This workshop will be investigating how AI technologies can be adapted or developed to address the following challenges: Discover events of interest and correlations in large amounts of science data; improve the outcomes of science modeling and data assimilation using improved data processing, integration, and analysis. Design advisors for mission planning and operations, including anomaly detection and spacecraft health monitoring. Develop tools for engineering support, including advanced manufacturing, orbit determination, new component design and system engineering. Customize intelligent user interfaces, including visual analytics and natural language processing.
Le Moigne, Jacqueline↗
Overview of Artificial Intelligence (AI) at NASA Goddard
Artificial Intelligence (AI) is a collection of advanced technologies that allows machines to think and act, both humanly and rationally, through sensing, comprehending, acting and learning. AI's foundations lie at the intersection of several traditional fields Philosophy, Mathematics, Economics, Neuroscience, Psychology and Computer Science. Although the inception of AI started in the 1950's, it has recently made a strong comeback in all aspects of society and all over the world; this is mainly due to the timely combination of increased data volumes, advanced and mature algorithms, and improvements in computing power and storage. Current AI applications include big data analytics, robotics, intelligent sensing, assisted decision making, and speech recognition just to name a few. During the Tour, we will show a few examples of the current AI activities at NASA Goddard.
Le Moigne, Jacqueline↗
Instrument Modeling Concepts for Tradespace Analysis of Satellite Constellations
Constellations are gaining popularity in government and commercial space-based missions for Earth Observation (EO) due to their risk tolerance and ability to improve observation sampling in space and time. NASA Goddard Space Flight Center (GSFC) is developing a pre-Phase A tool called Tradespace Analysis Tool for Constellations (TAT-C) to initiate constellation mission design. The tool will allow users to explore the tradespace between various performance, cost and risk metrics (as a function of their science mission) and select Pareto optimal architectures that meet their requirements. This paper will describe the concept of modeling the primary science instruments within TAT-C, using a radar as an example, but extendable to imagers, occulters and lidars. The modularity of TAT-C's software architecture allows for crisply defining the interface between TAT-C's user defined or internal variables and the payload variables. The described module will inform TAT-C users of payload-dependent performance differences among thousands of constellation architectures (e.g. revisit time of the sensor swath, differential signal to noise ratio (SNR), spatial resolution of measurements) and allow them to pick an appropriate constellation architecture for detailed development. The module may also inform operational decisions of satellite modes, based on ground optimization or onboard autonomy.
Nag, Sreeja↗
Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis
Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time.
Image Processing:Pattern Recognition↗
Evaluating Expected Performance and Graceful Degradation in Distributed Spacecraft Missions
Distributed Spacecraft Missions (DSMs) have largely garnered interest due to lower cost and new technical capabilities. However, another important attribute in DSMs is the potential of graceful degradation where a failure of one or more spacecraft (or instruments) in the DSM can reduce total performance, but the remaining functional spacecraft can maintain a limited science return. The potential for retaining some mission value in the event of partial failures leads to enhanced lifetime value. Here, an approach is developed to analyze graceful degradation and performance of DSM used for Earth observation. The system performance is defined through metrics of coverage and average revisit time of points of interest. Graceful degradation is quantified through total change and rate of change in performance metrics with increasing sub-system failures over the lifetime of a mission. An Expected Performance measure is formulated for conducting comparison of DSM architectures for early stage conceptual mission design and trade studies.
Distributed Spacecraft Missions↗
Trade-Space Analysis Tool for Designing Constellations (TAT-C)
While there is growing interest in implementing future NASA Earth Science missions as Distributed Spacecraft Missions (DSMs), there are currently no tool to help in the design of DSMs. The objective of our project is to provide a framework that facilitates DSM Pre-Phase A investigations and optimizes DSM designs with respect to a-priori Science goals. Our Trade-space Analysis Tool for Constellations (TAT-C) allows to investigate questions such as: "Which type of constellations should be chosen? How many spacecraft should be included in the constellation? Which design has the best cost/risk value?" This paper provides a description of the TAT-C tool and its components.
Image Processing↗
Improving Imaging Instrument Spatial Resolution Using Software
In order to overcome spatial resolution limitations associated with physical sensor limitations when using smallsats and cubesats, we utilize an image processing technology referred to as Super-Resolution (SR). In general, software approaches are increasingly considered in connection with smaller satellites for which size, mass and power constraints limit the sensor capabilities. Being able to perform hardware vs. software trades might enable more capabilities for a lower cost. This paper describes recent experiments conducted to optimize the spatial enhancement of acquired observations using multiple sub-pixel shifted low resolution image.
Image Processing↗
Distributed Spacecraft Missions (DSM) Technology Development at NASA Goddard Space Flight Center
For the last 5 years, NASA Goddard has been investigating Distributed Spacecraft Missions (DSM) system architectures, surveying past, current and potential mission concepts, developing several taxonomies and identifying some key technologies that will enable future DSM mission design, development, operations and management. This paper summarizes this Initiative and the talk will provide details about specific Goddard DSM projects that are currently underway and that are relevant to future Earth Science missions.
Le Moigne, Jacqueline↗
Distributed Spacecraft Missions (DSM) Technology Development at NASA Goddard Space Flight Center
For the last 5 years, NASA Goddard has been investigating Distributed Spacecraft Missions (DSM) system architectures, surveying past, current and potential mission concepts, developing several taxonomies and identifying some key technologies that will enable future DSM mission design, development, operations and management. This paper summarizes this Initiative and the talk will provide details about specific Goddard DSM projects that are currently underway and that are relevant to future Earth Science missions.
Distributed Spacecraft Missions↗
Improving the Spatial Resolution of Imaging Instruments Using Software
In order to overcome spatial resolution limitations associated with physical sensor limitations when using smallsats and cubesats, we utilize an image processing technology referred to as Super-Resolution (SR). In general, software approaches are increasingly considered in connection with smaller satellites for which size, mass and power constraints limit the sensor capabilities. Being able to perform hardware vs. software trades might enable more capabilities for a lower cost. This paper describes recent experiments conducted to optimize the spatial enhancement of acquired observations using multiple sub-pixel shifted low resolution image.
Image Processing↗
Towards the Development of a Global, Satellite-Based, Terrestrial Snow Mission Planning Tool
A global, satellite-based, terrestrial snow mission planning tool is proposed to help inform experimental mission design with relevance to snow depth and snow water equivalent (SWE). The idea leverages the capabilities of NASA's Land Information System (LIS) and the Tradespace Analysis Tool for Constellations (TAT-C) to harness the information content of Earth science mission data across a suite of hypothetical sensor designs, orbital configurations, data assimilation algorithms, and optimization and uncertainty techniques, including cost estimates and risk assessments of each hypothetical permutation. One objective of the proposed observing system simulation experiment (OSSE) is to assess the complementary or perhaps contradictory information content derived from the simultaneous collection of passive microwave (radiometer), active microwave (radar), and LIDAR observations from space-based platforms. The integrated system will enable a true end-to-end OSSE that can help quantify the value of observations based on their utility towards both scientific research and applications as well as to better guide future mission design. Science and mission planning questions addressed as part of this concept include: What observational records are needed (in space and time) to maximize terrestrial snow experimental utility? How might observations be coordinated (in space and time) to maximize this utility? What is the additional utility associated with an additional observation? How can future mission costs be minimized while ensuring Science requirements are fulfilled?
Mission Desig↗
End-to-End Trade-Space Analysis for Designing Constellation
Multipoint measurement missions can provide a significant advancement in science return and this science interest coupled with as many recent technological advances are driving a growing trend in exploring distributed architectures for future NASA missions. Distributed Spacecraft Missions (DSMs) leverage multiple spacecraft to achieve one or more common goals. In particular, a constellation is the most general form of DSM with two or more spacecraft placed into specific orbit(s) for the purpose of serving a common objective (e.g., CYGNSS). Because a DSM architectural trade-space includes both monolithic and distributed design variables, DSM optimization is a large and complex problem with multiple conflicting objectives. Over the last two years, our team has been developing a Trade-space Analysis Tool for Constellations (TAT-C), implemented in common programming languages for pre-Phase A constellation mission analysis. By evaluating alternative mission architectures, TAT-C seeks to minimize cost and maximize performance for pre-defined science goals. This presentation will describe the overall architecture of TAT-C including: a User Interface (UI) at several levels of details and user expertise; Trade-space Search Requests that are created from the Science requirements gathered by the UI and validated by a Knowledge Base; a Knowledge Base to compare the current requests to prior mission concepts to potentially prune the trade-space; a Trade-space Search Iterator which, with inputs from the Knowledge Base, and, in collaboration with the Orbit & Coverage, Reduction & Metrics, and Cost& Risk modules, generates multiple potential architectures and their associated characteristics. TAT-C leverages the use of the Goddard Mission Analysis Tool (GMAT) to compute coverage and ancillary data, modeling orbits to balance accuracy and performance. The current version includes uniform and non-uniform Walker constellations as well as Ad-Hoc and precessing constellations, and its cost model represents an aggregate model consisting of Cost Estimating Relationships (CERs) from widely accepted models. The current GUI automatically generates graphics representing metrics such as average revisit time or coverage as a function of cost. The end-to-end system will be demonstrated as part of the presentation.
Mission Desig↗