The Air Force Research Laboratory (AFRL) initiated the TechSat 21 flight experiment to demonstrate the concept of performing space missions for less overall cost using formations of small satellites that operate cooperatively to perform the function of a larger, single satellite. TechSat 21 is short for Technology Satellite of the 21st Century; the program began as a joint initiative of the Office of Scientific Research, Space Vehicles, Sensors, and Propulsion Directorates. Each satellite within the formation is designed to communicate with the others and share relative navigation, processing, payload operations, data downlii, and other mission functions. The flight experiment was conceived as 3 satellites flying in formation to create a large, recodigurable sparse aperture antenna system. The experiment was designed to address stressing challenges of distributed aperture radar and to provide a technology base for a variety of missions that include RF imaging, moving target indication, geolocation, anti-jamming, and terrain elevation. Key experiment objectives include demonstration of formation maintenance and reconfiguration, autonomous formation control, and multi-mission sparse aperture sensing. The Preliminary Design Review (PDR) was held in April 2001, and the Bus Critical Design Review (CDR) was held in Oct 2002. A concept analysis segment of the program addresses challenges related to the processing of sparse distributed aperture data, precision formation control, and cost performance analyses. An overview of the TechSat 21 concept, flight experiment objectives, and satellite description is presented along with a discussion of the challenges and solutions for implementing a distributed aperture RF antenna may in such areas as relative metrology, formation maintenance, and signal processing for varied mission applications. This paper also discusses the subsystem requirements derived fiom the desired performance goals and the hardware and software implementation necessary to achieve these requirements.
Abstract-Project Matsu is a collaboration between the OpenCommons Consortium and NASA focused on developing open source technology for the cloud-based processing of Earth satellite imagery. A particular focus is the development of applications for detecting fires and floods to help support natural disaster detection and relief. Project Matsu has developed an open source cloud-based infrastructure to process, analyze, and reanalyze large collections of hyperspectral satellite image data using OpenStack, Hadoop, MapReduce, Storm and related technologies.We describe a framework for efficient analysis of large amounts of data called the Matsu "Wheel." The Matsu Wheel is currently used to process incoming hyperspectral satellite data produced daily by NASA's Earth Observing-1 (EO-1) satellite. The framework is designed to be able to support scanning queries using cloud computing applications, such as Hadoop and Accumulo. A scanning query processes all, or most of the data, in a database or data repository.We also describe our preliminary Wheel analytics, including an anomaly detector for rare spectral signatures or thermal anomalies in hyperspectral data and a land cover classifier that can be used for water and flood detection. Each of these analytics can generate visual reports accessible via the web for the public and interested decision makers. The resultant products of the analytics are also made accessible through an Open Geospatial Compliant (OGC)-compliant Web Map Service (WMS) for further distribution. The Matsu Wheel allows many shared data services to be performed together to efficiently use resources for processing hyperspectral satellite image data and other, e.g., large environmental datasets that may be analyzed for many purposes.
Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for the cloud-based processing of Earth satellite imagery and for detecting fires and floods to help support natural disaster detection and relief. We describe a framework for efficient analysis and reanalysis of large amounts of data called the Matsu "Wheel" and the analytics used to process hyperspectral data produced daily by NASA's Earth Observing-1 (EO-1) satellite. The wheel is designed to be able to support scanning queries using cloud computing applications, such as Hadoop and Accumulo. A scanning query processes all, or most, of the data in a database or data repository. In contrast, standard queries typically process a relatively small percentage of the data. The wheel is a framework in which multiple scanning queries are grouped together and processed in turn, over chunks of data from the database or repository. Over time, the framework brings all data to each group of scanning queries. With this approach, contention and the overall time to process all scanning queries can be reduced. We describe our Wheel analytics, including an anomaly detector for rare spectral signatures or anomalies in hyperspectral data and a land cover classifier that can be This paper is an extension version of the BDS2016 accepted paper "The Matsu Wheel: A Cloud-based Framework for the Efficient Analysis and Reanalysis of Earth Satellite Imagery" [1].
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