Engineering topics
Publications and source records attributed to Frye, Stuart.
EO-1 Mission Status for the Constellation Mission Operations Working Group to discuss the EO-1 flight systems, mission enhancements, debris avoidance maneuver, orbital information, 5-year outlook, and new ground stations.
This presentation describes the closed loop satellite autonomy methods used to connect users and the assets on Earth Orbiter- 1 (EO-1) and similar satellites. The base layer is a distributed architecture based on Goddard Mission Services Evolution Concept (GMSEC) thus each asset still under independent control. Situational awareness is provided by a middleware layer through common Application Programmer Interface (API) to GMSEC components developed at GSFC. Users setup their own tasking requests, receive views into immediate past acquisitions in their area of interest, and into future feasibilities for acquisition across all assets. Automated notifications via pubsub feeds are returned to users containing published links to image footprints, algorithm results, and full data sets. Theme-based algorithms are available on-demand for processing.
Geosynchronous satellites offer a unique perspective for monitoring environmental factors important to understanding natural hazards and supporting the disasters management life cycle, namely forecast, detection, response, recovery and mitigation. In the NASA decadal survey for Earth science, the GEO-CAPE mission was proposed to address coastal and air pollution events in geosynchronous orbit, complementing similar initiatives in Asia by the South Koreans and by ESA in Europe, thereby covering the northern hemisphere. In addition to analyzing the challenges of identifying instrument capabilities to meet the science requirements, and the implications of hosting the instrument payloads on commercial geosynchronous satellites, the GEO-CAPE mission design team conducted a short study to explore strategies to optimize the science return for the coastal imaging instrument. The study focused on intelligent scheduling strategies that took into account cloud avoidance techniques as well as onboard processing methods to reduce the data storage and transmission loads. This paper expands the findings of that study to address the use of intelligent scheduling techniques and near-real time data product acquisition of both the coastal water and air pollution events. The topics include the use of onboard processing to refine and execute schedules, to detect cloud contamination in observations, and to reduce data handling operations. Analysis of state of the art flight computing capabilities will be presented, along with an assessment of cloud detection algorithms and their performance characteristics. Tools developed to illustrate operational concepts will be described, including their applicability to environmental monitoring domains with an eye to the future. In the geostationary configuration, the payload becomes a networked thing with enough connectivity to exchange data seamlessly with users. This allows the full field of view to be sensed at very high rate under the control of ground infrastructure, resulting in improved efficiencies, accuracy and science benefits. Hence a remote sensing payload and its data may become one of millions of connected objects in the emerging Internet of Things (IoT), and be as easily accessible by a users smart phone as any other smart appliance.
Data products derived from Earth observing satellites are difficult to find and share without specialized software and often times a highly paid and specialized staff. For our research effort, we endeavored to prototype a distributed architecture that depends on a standardized communication protocol and applications program interface (API) that makes it easy for anyone to discover and access disaster related data. Providers can easily supply the public with their disaster related products by building an adapter for our API. Users can use the API to browse and find products that relate to the disaster at hand, without a centralized catalogue, for example floods, and then are able to share that data via social media. Furthermore, a longerterm goal for this architecture is to enable other users who see the shared disaster product to be able to generate the same product for other areas of interest via simple point and click actions on the API on their mobile device. Furthermore, the user will be able to edit the data with on the ground local observations and return the updated information to the original repository of this information if configured for this function. This architecture leverages SensorWeb functionality [1] presented at previous IGARSS conferences. The architecture is divided into two pieces, the frontend, which is the GeoSocial API, and the backend, which is a standardized disaster node that knows how to talk to other disaster nodes, and also can communicate with the GeoSocial API. The GeoSocial API, along with the disaster node basic functionality enables crowdsourcing and thus can leverage insitu observations by people external to a group to perform tasks such as improving water reference maps, which are maps of existing water before floods. This can lower the cost of generating precision water maps. Keywords-Data Discovery, Disaster Decision Support, Disaster Management, Interoperability, CEOS WGISS Disaster Architecture
Over the past year few years, an international collaboration has developed a pilot project under the auspices of Committee on Earth Observation Satellite (CEOS) Disasters team. The overall team consists of civilian satellite agencies. For this pilot effort, the development team consists of NASA, Canadian Space Agency, Univ. of Maryland, Univ. of Colorado, Univ. of Oklahoma, Ukraine Space Research Institute and Joint Research Center(JRC) for European Commission. This development team collaborates with regional , national and international agencies to deliver end-to-end disaster coverage. In particular, the team in collaborating on this effort with the Namibia Department of Hydrology to begin in Namibia . However, the ultimate goal is to expand the functionality to provide early warning over the South Africa region. The initial collaboration was initiated by United Nations Office of Outer Space Affairs and CEOS Working Group for Information Systems and Services (WGISS). The initial driver was to demonstrate international interoperability using various space agency sensors and models along with regional in-situ ground sensors. In 2010, the team created a preliminary semi-manual system to demonstrate moving and combining key data streams and delivering the data to the Namibia Department of Hydrology during their flood season which typically is January through April. In this pilot, a variety of moderate resolution and high resolution satellite flood imagery was rapidly delivered and used in conjunction with flood predictive models in Namibia. This was collected in conjunction with ground measurements and was used to examine how to create a customized flood early warning system. During the first year, the team made use of SensorWeb technology to gather various sensor data which was used to monitor flood waves traveling down basins originating in Angola, but eventually flooding villages in Namibia. The team made use of standardized interfaces such as those articulated under the Open Cloud Consortium (OGC) Sensor Web Enablement (SWE) set of web services was good [1][2]. However, it was discovered that in order to make a system like this functional, there were many performance issues. Data sets were large and located in a variety of location behind firewalls and had to be accessed across open networks, so security was an issue. Furthermore, the network access acted as bottleneck to transfer map products to where they are needed. Finally, during disasters, many users and computer processes act in parallel and thus it was very easy to overload the single string of computers stitched together in a virtual system that was initially developed. To address some of these performance issues, the team partnered with the Open Cloud Consortium (OCC) who supplied a Computation Cloud located at the University of Illinois at Chicago and some manpower to administer this Cloud. The Flood SensorWeb [3] system was interfaced to the Cloud to provide a high performance user interface and product development engine. Figure 1 shows the functional diagram of the Flood SensorWeb. Figure 2 shows some of the functionality of the Computation Cloud that was integrated. A significant portion of the original system was ported to the Cloud and during the past year, technical issues were resolved which included web access to the Cloud, security over the open Internet, beginning experiments on how to handle surge capacity by using the virtual machines in the cloud in parallel, using tiling techniques to render large data sets as layers on map, interfaces to allow user to customize the data processing/product chain and other performance enhancing techniques. The conclusion reached from the effort and this presentation is that defining the interoperability standards in a small fraction of the work. For example, once open web service standards were defined, many users could not make use of the standards due to security restrictions. Furthermore, once an interoperable sysm is functional, then a surge of users can render a system unusable, especially in the disaster domain.
The Earth Observing One (EO-1) satellite was launched in November 2000 as a one year technology demonstration mission for a variety of space technologies. After the first year, it was used as a pathfinder for the creation of SensorWebs. A SensorWeb is the integration of variety of space, airborne and ground sensors into a loosely coupled collaborative sensor system that automatically provides useful data products. Typically, a SensorWeb is comprised of heterogeneous sensors tied together with a messaging architecture and web services. Disasters are the perfect arena to use SensorWebs. One SensorWeb pilot project that has been active since 2009 is the Namibia Early Flood Warning SensorWeb pilot project. The Pilot Project was established under the auspices of the Namibian Ministry of Agriculture Water and Forestry (MAWF)/Department of Water Affairs, the Committee on Earth Observing Satellites (CEOS)/Working Group on Information Systems and Services (WGISS) and moderated by the United Nations Platform for Space-based Information for Disaster Management and Emergency Response (UN-SPIDER). The effort began by identifying and prototyping technologies which enabled the rapid gathering and dissemination of both space-based and ground sensor data and data products for the purpose of flood disaster management and water-borne disease management. This was followed by an international collaboration to build small portions of the identified system which was prototyped during that past few years during the flood seasons which occurred in the February through May timeframe of 2010 and 2011 with further prototyping to occur in 2012. The SensorWeb system features EO-1 data along with other data sets from such satellites as Radarsat, Terra and Aqua. Finally, the SensorWeb team also began to examine the socioeconomic component to determine the impact of the SensorWeb technology and how best to assist in the infusion of this technology in lesser affluent areas with low levels of basic infrastructure. This paper provides an overview of these efforts, highlighting the EO-1 usage in this SensorWeb.
The Earth Observing One (EO-1) satellite was launched in November 2000 as a one year technology demonstration mission for a variety of space technologies. After the first year, in addition to collecting science data from its instruments, the EO-1 mission has been used as a testbed for a variety of technologies which provide various automation capabilities and which have been used as a pathfinder for the creation of SensorWebs. A SensorWeb is the integration of variety of space, airborne and ground sensors into a loosely coupled collaborative sensor system that automatically provides useful data products. Typically, a SensorWeb is comprised of heterogeneous sensors tied together with a messaging architecture and web services. This paper provides an overview of the various technologies that were tested and eventually folded into normal operations. As these technologies were folded in, the nature of operations transformed. The SensorWeb software enables easy connectivity for collaboration with sensors, but the side benefit is that it improved the EO-1 operational efficiency. This paper presents the various phases of EO-1 operation over the past 12 years and also presents operational efficiency gains demonstrated by some metrics.
No abstract available
A SensorWeb is a set of sensors, which can consist of ground, airborne and space-based sensors interoperating in an automated or autonomous collaborative manner. The NASA SensorWeb toolbox, developed at NASA/GSFC in collaboration with NASA/JPL, NASA/Ames and other partners, is a set of software and standards that (1) enables users to create virtual private networks of sensors over open networks; (2) provides the capability to orchestrate their actions; (3) provides the capability to customize the output data products and (4) enables automated delivery of the data products to the users desktop. A recent addition to the SensorWeb Toolbox is a new user interface, together with web services co-resident with the sensors, to enable rapid creation, loading and execution of new algorithms for processing sensor data. The web service along with the user interface follows the Open Geospatial Consortium (OGC) standard called Web Coverage Processing Service (WCPS). This presentation will detail the prototype that was built and how the WCPS was tested against a HyspIRI flight testbed and an elastic computation cloud on the ground with EO-1 data. HyspIRI is a future NASA decadal mission. The elastic computation cloud stores EO-1 data and runs software similar to Amazon online shopping.
This slide presentation reviews a system that uses the SensorWeb connected to the cloud computing, assist in disaster response, specifically flood warning in Namibia.
The major goal of the Namibia SensorWeb Pilot Project is a scientifically sound, operational trans-boundary flood management decision support system for Southern African region to provide useful flood and waterborne disease forecasting tools for local decision makers. The Pilot Project established under the auspices of: Namibian Ministry of Agriculture Water and Forestry (MAWF), Department of Water Affairs; Committee on Earth Observing Satellites (CEOS), Working Group on Information Systems and Services (WGISS); and moderated by the United Nations Platform for Space-based Information for Disaster Management and Emergency Response (UN-SPIDER). The effort consists of identifying and prototyping technology which enables the rapid gathering and dissemination of both space-based and ground sensor data and data products for the purpose of flood disaster management and water-borne disease management.
This research effort prototypes an implementation of a standard interface, Web Coverage Processing Service (WCPS), which is an Open Geospatial Consortium(OGC) standard, to enable users to define, test, upload and execute algorithms for on-orbit sensor systems. The user is able to customize on-orbit data products that result from raw data streaming from an instrument. This extends the SensorWeb 2.0 concept that was developed under a previous Advanced Information System Technology (AIST) effort in which web services wrap sensors and a standardized Extensible Markup Language (XML) based scripting workflow language orchestrates processing steps across multiple domains. SensorWeb 3G extends the concept by providing the user controls into the flight software modules associated with on-orbit sensor and thus provides a degree of flexibility which does not presently exist. The successful demonstrations to date will be presented, which includes a realistic HyspIRI decadal mission testbed. Furthermore, benchmarks that were run will also be presented along with future demonstration and benchmark tests planned. Finally, we conclude with implications for the future and how this concept dovetails into efforts to develop "cloud computing" methods and standards.
We describe a timeline-based scheduling algorithm developed for mission operations of the EO-1 earth observing satellite. We first describe the range of operational constraints for operations focusing on maneuver and thermal constraints that cannot be modeled in typical planner/schedulers. We then describe a greedy heuristic scheduling algorithm and compare its performance to both the prior scheduling algorithm - documenting an over 50% increase in scenes scheduled with estimated value of millions of dollars US. We also compare to a relaxed optimal scheduler showing that the greedy scheduler produces schedules with scene count within 15% of an upper bound on optimal schedules.
We have deployed and demonstrated operations of an integrated space in-situ sensorweb for monitoring volcanic activity. This sensorweb includes a network of ground sensors deployed to the Mount Saint Helens volcano as well as the Earth Observing One spacecraft. The ground operations and space operations are interlinked in that ground-based intelligent event detections can cause the space segment to acquire additional data via observation requests and space-based data acquisitions (thermal imagery) can trigger reconfigurations of the ground network to allocate increased bandwidth to areas of the network best situated to observe the activity. The space-based operations are enabled by an automated mission planning and tasking capability which utilizes several Opengeospatial Consortium (OGC) Sensorweb Enablement (SWE) standards which enable acquiring data, alerts, and tasking using web services. The ground-based segment also supports similar protocols to enable seamless tasking and data delivery. The space-based segment also supports onboard development of data products (thermal summary images indicating areas of activity, quicklook context images, and thermal activity alerts). These onboard developed products have reduced data volume (compared to the complete images) which enables them to be transmitted to the ground more rapidly in engineering channels.
We describe the modeling and reasoning about operations constraints in an automated mission planning system for an earth observing satellite - EO-1. We first discuss the large number of elements that can be naturally represented in an expressive planning and scheduling framework. We then describe a number of constraints that challenge the current state of the art in automated planning systems and discuss how we modeled these constraints as well as discuss tradeoffs in representation versus efficiency. Finally we describe the challenges in efficiently generating operations plans for this mission. These discussions involve lessons learned from an operations model that has been in use since Fall 2004 (called R4) as well as a newer more accurate operations model operational since June 2009 (called R5). We present analysis of the R5 software documenting a significant (greater than 50%) increase in the number of weekly observations scheduled by the EO-1 mission. We also show that the R5 mission planning system produces schedules within 15% of an upper bound on optimal schedules. This operational enhancement has created value of millions of dollars US over the projected remaining lifetime of the EO-1 mission.
Remote-sensed hyperspectral data represents significant challenges in downlink due to its large data volumes. This paper describes a research program designed to process hyperspectral data products onboard spacecraft to (a) reduce data downlink volumes and (b) decrease latency to provide key data products (often by enabling use of lower data rate communications systems). We describe efforts to develop onboard processing to study volcanoes, floods, and cryosphere, using the Hyperion hyperspectral imager and onboard processing for the Earth Observing One (EO-1) mission as well as preliminary work targeting the Hyperspectral Infrared Imager (HyspIRI) mission.
We have been operating an autonomous, integrated sensorweb linking numerous space and ground sensors in 24/7 operations since 2004. This sensorweb includes elements of space data acquisition (MODIS, GOES, and EO-1), space asset retasking (EO-1), integration of data acquired from ground sensor networks with on-demand ground processing of data into science products. These assets are being integrated using web service standards from the Open Geospatial Consortium. Future plans include extension to fixed and mobile surface and subsurface sea assets as part of the NSF's ORION Program.
We discuss a current, ongoing demonstration of insitu onboard detection in which the Earth Observing-1 spacecraft detects surface sulfur deposits that originate from underlying springs by distinguishing the sulfur from the ice-rich glacial background, a good analogue for the Europan surface. In this paper, we describe the process of developing the onboard classifier for detecting the presence of sulfur in a hyperspectral scene, including the use of a training/testing set that is not exhaustively labeled, i.e.not all true positives are marked, and the selection of 12, out of 242, Hyperion instrument wavelength bands to use in the onboard detector. This study aims to demonstrate the potential for future missions to capture short-lived science events, make decisions onboard, identify high priority data for downlink and perform onboard change detection. In the future, such capability could help maximize the science return of downlink bandwidth-limited missions, addressing a significant constraint in all deep-space missions.