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Grosvenor, Sandy

Publications and source records attributed to Grosvenor, Sandy.

17 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Coordinating an Autonomous Earth-Observing Sensorweb

A system of software has been developed to coordinate the operation of an autonomous Earth-observing sensorweb. Sensorwebs are collections of sensor units scattered over large regions to gather data on spatial and temporal patterns of physical, chemical, or biological phenomena in those regions. Each sensor unit is a node in a data-gathering/ data-communication network that spans a region of interest. In this case, the region is the entire Earth, and the sensorweb includes multiple terrestrial and spaceborne sensor units. In addition to acquiring data for scientific study, the sensorweb is required to give timely notice of volcanic eruptions, floods, and other hazardous natural events. In keeping with the inherently modular nature of the sensory, communication, and data-processing hardware, the software features a flexible, modular architecture that facilitates expansion of the network, customization of conditions that trigger alarms of hazardous natural events, and customization of responses to alarms. The soft8 NASA Tech Briefs, July 2006 ware facilitates access to multiple sources of data on an event of scientific interest, enables coordinated use of multiple sensors in rapid reaction to detection of an event, and facilitates the tracking of spacecraft operations, including tracking of the acquisition, processing, and downlinking of requested data.

Sherwood, Robert↗
NASA NTRS2004

Using automated planning for sensorweb response

This paper describes efforts to link these science event detection systems with an automated response system to retarget remote sensing assets to observe these important but transient science events. Of course, automated mission planning is a key element of the overall tracking and response system. We describe the current prototype system which utilizes the Earth Observing One spacecraft, MODIS flying on Terra and Aqua, QuickSCAT, GOES, and AVHRR platforms as well as future plans for expansion.

sensorweb response↗
NASA NTRS2004

Autonomous Multi-Sensor Coordination: The Science Goal Monitor

Many dramatic earth phenomena are dynamic and coupled. In order to fully understand them, we need to obtain timely coordinated multi-sensor observations from widely dispersed instruments. Such a dynamic observing system must include the ability to Schedule flexibly and react autonomously to sciencehser driven events; Understand higher-level goals of a sciencehser defined campaign; Coordinate various space-based and ground-based resources/sensors effectively and efficiently to achieve goals. In order to capture transient events, such a 'sensor web' system must have an automated reactive capability built into its scientific operations. To do this, we must overcome a number of challenges inherent in infusing autonomy. The Science Goal Monitor (SGM) is a prototype software tool being developed to explore the nature of automation necessary to enable dynamic observing. The tools being developed in SGM improve our ability to autonomously monitor multiple independent sensors and coordinate reactions to better observe dynamic phenomena. The SGM system enables users to specify what to look for and how to react in descriptive rather than technical terms. The system monitors streams of data to identify occurrences of the key events previously specified by the scientisther. When an event occurs, the system autonomously coordinates the execution of the users' desired reactions between different sensors. The information can be used to rapidly respond to a variety of fast temporal events. Investigators will no longer have to rely on after-the-fact data analysis to determine what happened. Our paper describes a series of prototype demonstrations that we have developed using SGM and NASA's Earth Observing-1 (EO-1) satellite and Earth Observing Systems' Aqua/Terra spacecrafts' MODIS instrument. Our demonstrations show the promise of coordinating data from different sources, analyzing the data for a relevant event, autonomously updating and rapidly obtaining a follow-on relevant image. SGM was used to investigate forest fires, floods and volcanic eruptions. We are now identifying new Earth science scenarios that will have more complex SGM reasoning. By developing and testing a prototype in an operational environment, we are also establishing and gathering metrics to gauge the success of automating science campaigns.

Koratkar, Anuradha↗
NASA NTRS2004

Science Goal Monitor: Science Goal Driven Automation for NASA Missions

Infusion of automation technologies into NASA s future missions will be essential because of the need to: (1) effectively handle an exponentially increasing volume of scientific data, (2) successfully meet dynamic, opportunistic scientific goals and objectives, and (3) substantially reduce mission operations staff and costs. While much effort has gone into automating routine spacecraft operations to reduce human workload and hence costs, applying intelligent automation to the science side, i.e., science data acquisition, data analysis and reactions to that data analysis in a timely and still scientifically valid manner, has been relatively under-emphasized. In order to introduce science driven automation in missions, we must be able to: capture and interpret the science goals of observing programs, represent those goals in machine interpretable language; and allow spacecrafts onboard systems to autonomously react to the scientist's goals. In short, we must teach our platforms to dynamically understand, recognize, and react to the scientists goals. The Science Goal Monitor (SGM) project at NASA Goddard Space Flight Center is a prototype software tool being developed to determine the best strategies for implementing science goal driven automation in missions. The tools being developed in SGM improve the ability to monitor and react to the changing status of scientific events. The SGM system enables scientists to specify what to look for and how to react in descriptive rather than technical terms. The system monitors streams of science data to identify occurrences of key events previously specified by the scientist. When an event occurs, the system autonomously coordinates the execution of the scientist s desired reactions. Through SGM, we will improve om understanding about the capabilities needed onboard for success, develop metrics to understand the potential increase in science returns, and develop an operational prototype so that the perceived risks associated with increased use of automation can be reduced.

Koratkar, Anuradha↗
NASA NTRS2004

Autonomous Multi-sensor Coordination: The Science Goal Monitor

Next-generation science and exploration systems will employ new observation strategies that will use multiple sensors in a dynamic environment to provide high quality monitoring, self-consistent analyses and informed decision making. The Science Goal Monitor (SGM) is a prototype software tool being developed to explore the nature of automation necessary to enable dynamic observing of earth phenomenon. The tools being developed in SGM improve our ability to autonomously monitor multiple independent sensors and coordinate reactions to better observe the dynamic phenomena. The SGM system enables users to specify events of interest and how to react when an event is detected. The system monitors streams of data to identify occurrences of the key events previously specified by the scientist/user. When an event occurs, the system autonomously coordinates the execution of the users desired reactions between different sensors. The information can be used to rapidly respond to a variety of fast temporal events. Investigators will no longer have to rely on after-the-fact data analysis to determine what happened. Our paper describes a series of prototype demonstrations that we have developed using SGM and NASA's Earth Observing-1 (EO-1) satellite and Earth Observing Systems Aqua/Terra spacecrafts MODIS instrument. Our demonstrations show the promise of coordinating data from different sources, analyzing the data for a relevant event, autonomously updating and rapidly obtaining a follow-on relevant image. SGM is being used to investigate forest fires, floods and volcanic eruptions. We are now identifying new earth science scenarios that will have more complex SGM reasoning. By developing and testing a prototype in an operational environment, we are also establishing and gathering metrics to gauge the success of automating science campaigns.

Koratkar, Anuradha↗
NASA NTRS2003

Science Goal Driven Automation for NASA Missions: The Science Goal Monitor

Infusion of automation technologies into NASA s future missions will be essential not only to achieve substantial reduction in mission operations staff and costs, but also in order to both effectively handle an exponentially increasing volume of scientific data and to successfully meet dynamic, opportunistic scientific goals and objectives. Current spacecraft operations cannot respond to science driven events, such as intrinsically variable or short-lived phenomena in a timely manner. For such investigations, we must teach our platforms to dynamically understand, recognize, and react to the scientists goals. While much effort has gone into automating routine spacecraft operations to reduce human workload and hence costs, applying intelligent automation to the science side, i.e., science data acquisition, data analysis and reactions to that data analysis in a timely and still scientifically valid manner, has been relatively under-emphasized.

Korathkar, Anuradha↗
NASA NTRS2002

An End-to-End Architecture for Science Goal Driven Observing

New observatories will have greater on-board storage capacity and on-board processing capabilities. The new bottleneck will be download capacity. The cost of downlink time and limitations of bandwidth will end the era where all exposure data is downloaded and all data processing is performed on the ground. In addition, observing campaigns involving inherently variable targets will need scheduling flexibility to focus observing time and data download on exposures that are scientifically interesting. The ability to quickly recognize and react to such events by re-prioritizing the observing schedule will be an essential characteristic for maximizing scientific returns. It will also be a step towards increasing spacecraft autonomy, a major goal of NASA's strategic plan. The science goal monitoring (SGM) system is a proof-of-concept effort to address these challenges. We are developing an interactive distributed system that will use on-board processing and storage combined with event-driven interfaces with ground-based processing and operations, to enable fast re-prioritization of observing schedules, and to minimize time spent on non-optimized observations. SGM is initially aimed towards time-tagged observing modes used frequently in spectroscopic studies of varying targets. In particular, the SGM is collaborating with the proposed MIDEX-class mission Kronos team. The variable targets that Kronos seeks to study make an adaptive system such as SGM particularly valuable for achieving mission goals. However, the architecture and interfaces will also be designed for easy adaptability to other observing platforms, including ground-based systems and to work with different scheduling and pipeline processing systems. This talk will focus on our strategy for developing SGM and the technical challenges that we have encountered. We will discuss the SGM architecture as it applies to the Kronos mission and explain how it is scalable to other missions.

Jones, Jeremy↗
NASA NTRS2002

Science Goal Driven Observing: A Step Towards Maximizing Science Returns and Spacecraft Autonomy

While recent missions have made great strides in the ability to autonomously monitor and react to changing health and physical status of spacecraft, little progress has been made in responding quickly to science driven events. Also, the new generation of space-based telescopes will generate data at an unprecedented rate. Yet, while on-board data processing and storage capability will increase rapidly, bandwidth for downloading data will not increase as fast and can become a significant cost of a science program. For observations of inherently variable targets and targets of opportunity, the ability to recognize early if an observation will not meet the science goals, and react accordingly, can have a major positive impact on the overall scientific returns of an observatory and on its operational costs. If the observatory can reprioritize the schedule to focus on alternate targets, discard uninteresting observations prior to downloading, or download them at a reduced resolution its overall efficiency will be dramatically increased. We are investigating and developing tools for a science goal monitoring (SGM) system. The SGM will have an interface to help capture higher-level science goals from the scientists and translate them into a flexible observing strategy that SGM can execute and monitor. SGM will then monitor the incoming data stream and interface with data processing systems to recognize significant events. When an event occurs, the system will use the science goals given it to reprioritize observations, and react appropriately and/or communicate with ground systems - both human and machine - for confirmation and/or further high priority analyses.

Jones, Jeremy↗
NASA NTRS2002

Science Goal Driven Observing and Spacecraft Autonomy

Spacecraft autonomy will be an integral part of mission operations in the coming decade. While recent missions have made great strides in the ability to autonomously monitor and react to changing health and physical status of spacecraft, little progress has been made in responding quickly to science driven events. For observations of inherently variable targets and targets of opportunity, the ability to recognize early if an observation will meet the science goals of a program, and react accordingly, can have a major positive impact on the overall scientific returns of an observatory and on its operational costs. If the onboard software can reprioritize the schedule to focus on alternate targets, discard uninteresting observations prior to downloading, or download a subset of observations at a reduced resolution, the spacecraft's overall efficiency will be dramatically increased. The science goal monitoring (SGM) system is a proof- of-concept effort to address the above challenge. The SGM will have an interface to help capture higher-level science goals from the scientists and translate them into a flexible observing strategy that SGM can execute and monitor. We are developing an interactive distributed system that will use on-board processing and storage combined with event-driven interfaces with ground-based processing and operations, to enable fast re-prioritization of observing schedules, and to minimize time spent on non-optimized observations. This paper will focus on our strategy for developing SGM and the technical challenges that we have encountered. We will discuss the SGM architecture as it applies to the proposed MIDEX-class mission Kronos. However, the architecture and interfaces will also be designed for easy adaptability to other observing platforms, including ground-based systems and to work with different scheduling and pipeline processing systems.

Jones, Jeremy↗
NASA NTRS2002

Science Goal Driven Observing and Spacecraft Autonomy

Spacecraft autonomy will be an integral part of mission operations in the coming decade. While recent missions have made great strides in the ability to autonomously monitor and react to changing health and physical status of spacecraft, little progress has been made in responding quickly to science driven events. For observations of inherently variable targets and targets of opportunity, the ability to recognize early if an observation will meet the science goals of a program, and react accordingly, can have a major positive impact on the overall scientific returns of an observatory and on its operational costs. If the onboard software can reprioritize the schedule to focus on alternate targets, discard uninteresting observations prior to downloading, or download a subset of observations at a reduced resolution, the spacecraft's overall efficiency will be dramatically increased. The science goal monitoring (SGM) system is a proof-of-concept effort to address the above challenge. The SGM will have an interface to help capture higher level science goals from the scientists and translate them into a flexible observing strategy that SGM can execute and monitor. We are developing an interactive distributed system that will use on-board processing and storage combined with event-driven interfaces with ground-based processing and operations, to enable fast re-prioritization of observing schedules, and to minimize time spent on non-optimized observations.

Koratkar, Amuradha↗
NASA NTRS2002

Science Goal Driven Observing: A Step Towards Maximizing Science Returns and Spacecraft Autonomy

In the coming decade, the drive to increase the scientific returns on capital investment and to reduce costs will force automation to be implemented in many of the scientific tasks that have traditionally been manually overseen. Thus, spacecraft autonomy will become an even greater part of mission operations. While recent missions have made great strides in the ability to autonomously monitor and react to changing health and physical status of spacecraft, little progress has been made in responding quickly to science driven events. The new generation of space-based telescopes/observatories will see deeper, with greater clarity, and they will generate data at an unprecedented rate. Yet, while onboard data processing and storage capability will increase rapidly, bandwidth for downloading data will not increase as fast and can become a significant bottleneck and cost of a science program. For observations of inherently variable targets and targets of opportunity, the ability to recognize early if an observation will not meet the science goals of variability or minimum brightness, and react accordingly, can have a major positive impact on the overall scientific returns of an observatory and on its operational costs. If the observatory can reprioritize the schedule to focus on alternate targets, discard uninteresting observations prior to downloading, or download them at a reduced resolution its overall efficiency will be dramatically increased. We are investigating and developing tools for a science goal monitoring (SGM) system. The SGM will have an interface to help capture higher-level science goals from scientists and translate them into a flexible observing strategy that SGM can execute and monitor. SGM will then monitor the incoming data stream and interface with data processing systems to recognize significant events. When an event occurs, the system will use the science goals given it to reprioritize observations, and react appropriately and/or communicate with ground systems - both human and machine - for confirmation and/or further high priority analyses.

Koratkar, Anuradha↗
NASA NTRS2001

Code Sharing and Collaboration: Experiences From the Scientist's Expert Assistant Project and Their Relevance to the Virtual Observatory

In the Virtual Observatory (VO), software tools will perform the functions that have traditionally been performed by physical observatories and their instruments. These tools will not be adjuncts to VO functionality but will make up the very core of the VO. Consequently, the tradition of observatory and system independent tools serving a small user base is not valid for the VO. For the VO to succeed, we must improve software collaboration and code sharing between projects and groups. A significant goal of the Scientist's Expert Assistant (SEA) project has been promoting effective collaboration and code sharing among groups. During the past three years, the SEA project has been developing prototypes for new observation planning software tools and strategies. Initially funded by the Next Generation Space Telescope, parts of the SEA code have since been adopted by the Space Telescope Science Institute. SEA has also supplied code for the SIRTF (Space Infrared Telescope Facility) planning tools, and the JSky Open Source Java library. The potential benefits of sharing code are clear. The recipient gains functionality for considerably less cost. The provider gains additional developers working with their code. If enough users groups adopt a set of common code and tools, de facto standards can emerge (as demonstrated by the success of the FITS standard). Code sharing also raises a number of challenges related to the management of the code. In this talk, we will review our experiences with SEA--both successes and failures, and offer some lessons learned that might promote further successes in collaboration and re-use.

Korathkar, Anuradha↗
NASA NTRS2001

Linking Science Analysis with Observation Planning: A Full Circle Data Lifecycle

A clear goal of the Virtual Observatory (VO) is to enable new science through analysis of integrated astronomical archives. An additional and powerful possibility of the VO is to link and integrate these new analyses with planning of new observations. By providing tools that can be used for observation planning in the VO, the VO will allow the data lifecycle to come full circle: from theory to observations to data and back around to new theories and new observations. The Scientist's Expert Assistant (SEA) Simulation Facility (SSF) is working to combine the ability to access existing archives with the ability to model and visualize new observations. Integrating the two will allow astronomers to better use the integrated archives of the VO to plan and predict the success of potential new observations more efficiently, The full circle lifecycle enabled by SEA can allow astronomers to make substantial leaps in the quality of data and science returns on new observations. Our paper examines the exciting potential of integrating archival analysis with new observation planning, such as performing data calibration analysis on archival images and using that analysis to predict the success of new observations, or performing dynamic signal-to-noise analysis combining historical results with modeling of new instruments or targets. We will also describe how the development of the SSF is progressing and what have been its successes and challenges.

Grosvenor, Sandy↗
NASA NTRS2001

Linking Science Analysis with Observation Planning: A Full Circle Data Lifecycle

A clear goal of the Virtual Observatory (VO) is to enable new science through analysis of integrated astronomical archives. An additional and powerful possibility of the VO is to link and integrate these new analyses with planning of new observations. By providing tools that can be used for observation planning in the VO, the VO will allow the data lifecycle to come full circle: from theory to observations to data and back around to new theories and new observations. The Scientist's Expert Assistant (SEA) Simulation Facility (SSF) is working to combine the ability to access existing archives with the ability to model and visualize new observations. Integrating the two will allow astronomers to better use the integrated archives of the VO to plan and predict the success of potential new observations. The full circle lifecycle enabled by SEA can allow astronomers to make substantial leaps in the quality of data and science returns on new observations. Our paper will examine the exciting potential of integrating archival analysis with new observation planning, such as performing data calibration analysis on archival images and using that analysis to predict the success of new observations, or performing dynamic signal-to-noise analysis combining historical results with modeling of new instruments or targets. We will also describe how the development of the SSF is progressing and what has been its successes and challenges.

Jones, Jeremy↗
NASA NTRS2001

Code Sharing and Collaboration: Experiences from the Scientist's Expert Assistant Project and their Relevance to the Virtual Observatory

In the Virtual Observatory (VO), software tools will perform the functions that have traditionally been performed by physical observatories and their instruments. These tools will not be adjuncts to VO functionality but will make up the very core of the VO. Consequently, the tradition of observatory and system independent tools serving a small user base is not valid for the VO. For the VO to succeed, we must improve software collaboration and code sharing between projects and groups. A significant goal of the Scientist's Expert Assistant (SEA) project has been promoting effective collaboration and code sharing between groups. During the past three years, the SEA project has been developing prototypes for new observation planning software tools and strategies. Initially funded by the Next Generation Space Telescope, parts of the SEA code have since been adopted by the Space Telescope Science Institute. SEA has also supplied code for SOFIA, the SIRTF planning tools, and the JSky Open Source Java library. The potential benefits of sharing code are clear. The recipient gains functionality for considerably less cost. The provider gains additional developers working with their code. If enough users groups adopt a set of common code and tools, defacto standards can emerge (as demonstrated by the success of the FITS standard). Code sharing also raises a number of challenges related to the management of the code. In this talk, we will review our experiences with SEA - both successes and failures - and offer some lessons learned that may promote further successes in collaboration and re-use.

Jones, Jeremy↗