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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

MAGSAT data processing: A report for investigators

The in-flight attitude and vector magnetometer data bias recovery techniques and results are described. The attitude bias recoveries are based on comparisons with a magnetic field model and are thought to be accurate to 20 arcsec. The vector magnetometer bias recoveries are based on comparisons with the scalar magnetometer data and are thought to be accurate to 3 nT or better. The MAGSAT position accuracy goals of 60 m radially and 300 m horizontally were achieved for all but the last 3 weeks of Magsat lifetime. This claim is supported by ephemeris overlap statistics and by comparisons with ephemerides computed with an independent orbit program using data from an independent tracking network. MAGSAT time determination accuracy is estimated at 1 ms. Several errors in prelaunch assumptions regarding data time tags, which escaped detection in prelaunch data tests, and were discovered and corrected postlaunch are described. Data formats and products, especially the Investigator-B tapes, which contain auxiliary parameters in addition to the basic magnetometer and ephemeris data, are described.

Langel, R. A.↗
NASA NTRS2016

The Kepler Science Data Processing Pipeline Source Code Road Map

We give an overview of the operational concepts and architecture of the Kepler Science Processing Pipeline. Designed, developed, operated, and maintained by the Kepler Science Operations Center (SOC) at NASA Ames Research Center, the Science Processing Pipeline is a central element of the Kepler Ground Data System. The SOC consists of an office at Ames Research Center, software development and operations departments, and a data center which hosts the computers required to perform data analysis. The SOC's charter is to analyze stellar photometric data from the Kepler spacecraft and report results to the Kepler Science Office for further analysis. We describe how this is accomplished via the Kepler Science Processing Pipeline, including, the software algorithms. We present the high-performance, parallel computing software modules of the pipeline that perform transit photometry, pixel-level calibration, systematic error correction, attitude determination, stellar target management, and instrument characterization.

Kepler pipeline software↗
NASA NTRS1988

Spacelab data processing facility (SLDPF) Quality Assurance (QA)/Data Accounting (DA) expert systems: Transition from prototypes to operational systems

The SLDPF is responsible for the capture, quality monitoring processing, accounting, and shipment of Spacelab and/or Attached Shuttle Payloads (ASP) telemetry data to various user facilities. Expert systems will aid in the performance of the quality assurance and data accounting functions of the two SLDPF functional elements: the Spacelab Input Processing System (SIPS) and the Spacelab Output Processing System (SOPS). Prototypes were developed for each as independent efforts. The SIPS Knowledge System Prototype (KSP) used the commercial shell OPS5+ on an IBM PC/AT; the SOPS Expert System Prototype used the expert system shell CLIPS implemented on a Macintosh personal computer. Both prototypes emulate the duties of the respective QA/DA analysts based upon analyst input and predetermined mission criteria parameters, and recommended instructions and decisions governing the reprocessing, release, or holding for further analysis of data. These prototypes demonstrated feasibility and high potential for operational systems. Increase in productivity, decrease of tedium, consistency, concise historial records, and a training tool for new analyses were the principal advantages. An operational configuration, taking advantage of the SLDPF network capabilities, is under development with the expert systems being installed on SUN workstations. This new configuration in conjunction with the potential of the expert systems will enhance the efficiency, in both time and quality, of the SLDPF's release of Spacelab/AST data products.

Basile, Lisa↗
NASA NTRS1988

Spacelab data processing facility (SLDPF) quality assurance (QA)/data accounting (DA) expert systems - Transition from prototypes to operational systems

The SLDPF is responsible for the capture, quality monitoring processing, accounting, and shipment of Spacelab and/or Attached Shuttle Payloads (ASP) telemetry data to various user facilities. Expert systems will aid in the performance of the quality assurance and data accounting functions of the two SLDPF functional elements: the Spacelab Input Processing System (SIPS) and the Spacelab Output Processing System (SOPS). Prototypes were developed for each as independent efforts. The SIPS Knowledge System Prototype (KSP) used the commercial shell OPS5+ on an IBM PC/AT; the SOPS Expert System Prototype used the expert system shell CLIPS implemented on a Macintosh personal computer. Both prototypes emulate the duties of the respective QA/DA analysts based upon analyst input and predetermined mission criteria parameters, and recommended instructions and decisions governing the reprocessing, release, or holding for further analysis of data. These prototypes demonstrated feasibility and high potential for operational systems. Increase in productivity, decrease of tedium, consistency, concise historical records, and a training tool for new analyses were the principal advantages. An operational configuration, taking advantage of the SLDPF network capabilities, is under development with the expert systems being installed on SUN workstations. This new configuration in conjunction with the potential of the expert systems will enhance the efficiency, in both time and quality, of the SLDPF's release of Spacelab/AST data products.

Basile, Lisa↗
NASA NTRS2013

Using NASA's Reference Architecture: Comparing Polar and Geostationary Data Processing Systems

The JPSS and GOES-R programs are housed at NASA GSFC and jointly implemented by NASA and NOAA to NOAA requirements. NASA's role in the JPSS Ground System is to develop and deploy the system according to NOAA requirements. NASA's role in the GOES-R ground segment is to provide Systems Engineering expertise and oversight for NOAA's development and deployment of the system. NASA's Earth Science Data Systems Reference Architecture is a document developed by NASA's Earth Science Data Systems Standards Process Group that describes a NASA Earth Observing Mission Ground system as a generic abstraction. The authors work within the respective ground segment projects and are also separately contributors to the Reference Architecture document. Opinions expressed are the author's only and are not NOAA, NASA or the Ground Projects' official positions.

Ullman, Richard↗
NASA NTRS2009

Data Processing for Atmospheric Phase Interferometers

This paper presents a detailed discussion of calibration procedures used to analyze data recorded from a two-element atmospheric phase interferometer (API) deployed at Goldstone, California. In addition, we describe the data products derived from those measurements that can be used for site intercomparison and atmospheric modeling. Simulated data is used to demonstrate the effectiveness of the proposed algorithm and as a means for validating our procedure. A study of the effect of block size filtering is presented to justify our process for isolating atmospheric fluctuation phenomena from other system-induced effects (e.g., satellite motion, thermal drift). A simulated 24 hr interferometer phase data time series is analyzed to illustrate the step-by-step calibration procedure and desired data products.

Acosta, Roberto J.↗
NASA NTRS1985

Atmospheric phenomena data processing and display

Several related efforts which deal with the analysis and display of data pertaining to various atmospheric phenomena are reported. Spectrometer data analysis, with refinements to the preexisting programs, is discussed. A discussion of a Monte Carlo simulation of a photon transfer problem is presented. Raw data gathered in the field by a high-flying U2 aircraft is usually pulse code modulated (PCM). Techniques to reduce the data into a computer-amenable format are described. Several utility programs and other work are outlined.

Tatom, F. B.↗
NASA NTRS1976

Architecture and data processing alternatives for Tse computer. Volume 1: Tse logic design concepts and the development of image processing machine architectures

Digital computer components which perform two dimensional array logic operations (Tse logic) on binary data arrays are described. The properties of Golay transforms which make them useful in image processing are reviewed, and several architectures for Golay transform processors are presented with emphasis on the skeletonizing algorithm. Conventional logic control units developed for the Golay transform processors are described. One is a unique microprogrammable control unit that uses a microprocessor to control the Tse computer. The remaining control units are based on programmable logic arrays. Performance criteria are established and utilized to compare the various Golay transform machines developed. A critique of Tse logic is presented, and recommendations for additional research are included.

Rickard, D. A.↗
NASA NTRS1975

Data processing large quantities of multispectral information

Method is combination of digital and optical techniques. Multispectral data is coded into binary matrix format and then encoded onto photographic film. Film is holographically correlated with spectral signature to generate single-class classification map. Number of maps are optically superimposed to produce full-color, multiclass classification map.

Haskell, R. E.↗
NASA NTRS1974

Multispectral scanner data processing over Sam Houston National Forest

The Edit 9 forest scene, a computer processing technique, and its capability to map timber types in the Sam Houston National Forest, are evaluated. Special efforts were made to evaluate existing computer processing techniques in mapping timber types using ERTS-1 and aircraft data, and to provide an opportunity to open up new research and development areas in forestry data.

Reeves, C. A.↗
NASA NTRS1976

Radar data processing and analysis

Digitized four-channel radar images corresponding to particular areas from the Phoenix and Huntington test sites were generated in conjunction with prior experiments performed to collect X- and L-band synthetic aperture radar imagery of these two areas. The methods for generating this imagery are documented. A secondary objective was the investigation of digital processing techniques for extraction of information from the multiband radar image data. Following the digitization, the remaining resources permitted a preliminary machine analysis to be performed on portions of the radar image data. The results, although necessarily limited, are reported.

Ausherman, D.↗
NASA NTRS1981

Digital ultrasonics signal processing: Flaw data post processing use and description

A modular system composed of two sets of tasks which interprets the flaw data and allows compensation of the data due to transducer characteristics is described. The hardware configuration consists of two main units. A DEC LSI-11 processor running under the RT-11 sngle job, version 2C-02 operating system, controls the scanner hardware and the ultrasonic unit. A DEC PDP-11/45 processor also running under the RT-11, version 2C-02, operating system, stores, processes and displays the flaw data. The software developed the Ultrasonics Evaluation System, is divided into two catagories; transducer characterization and flaw classification. Each category is divided further into two functional tasks: a data acquisition and a postprocessor ask. The flaw characterization collects data, compresses its, and writes it to a disk file. The data is then processed by the flaw classification postprocessing task. The use and operation of a flaw data postprocessor is described.

Buel, V. E.↗
NASA NTRS1979

Heat Capacity Mapping Radiometer (HCMR) data processing algorithm, calibration, and flight performance evaluation

The rationale and procedures used in the radiometric calibration and correction of Heat Capacity Mapping Mission (HCMM) data are presented. Instrument-level testing and calibration of the Heat Capacity Mapping Radiometer (HCMR) were performed by the sensor contractor ITT Aerospace/Optical Division. The principal results are included. From the instrumental characteristics and calibration data obtained during ITT acceptance tests, an algorithm for post-launch processing was developed. Integrated spacecraft-level sensor calibration was performed at Goddard Space Flight Center (GSFC) approximately two months before launch. This calibration provided an opportunity to validate the data calibration algorithm. Instrumental parameters and results of the validation are presented and the performances of the instrument and the data system after launch are examined with respect to the radiometric results. Anomalies and their consequences are discussed. Flight data indicates a loss in sensor sensitivity with time. The loss was shown to be recoverable by an outgassing procedure performed approximately 65 days after the infrared channel was turned on. It is planned to repeat this procedure periodically.

Bohse, J. R.↗
NASA NTRS1985

Analysis and Evaluation of the LANDSAT-4 MSS and TM Sensors and Ground Data Processing Systems: Early Results

The MSS and TM sensor performances were evaluated by studying both the sensors and the characteristics of the data. Information content analysis, image statistics, band-to-band registration, the presence of failed or failing detectors, and sensor resolution are discussed. The TM data were explored from the point of view of adequacy of the ground processing and improvements that could be made to compensate for sensor problems and deficiencies. Radiometric correction processing, compensation for a failed detector, and geometric correction processing are also considered.

Bernstein, R.↗
NASA NTRS1982

Image-data-processing for earth resources - An overview

A total remote-sensing system comprises four distinct operations: data-acquisition, archiving, production, and dissemination of individual products, and extraction of the required information. The Landsat satellite system acquires data in several spectral bands, with a ground resolution of 80 meters (currently) or 30 meters (future). Radiometric and geometric corrections are normally applied, after which the data may be enhanced for photointerpretation or analyzed by computer. The accuracy of the analysis model for either is important. The extraction of information from remotely sensed data will generally be dependent on the use of it with other data such as maps or, perhaps, weather information. Methods are being developed to use the various data together in geographic data systems. The availability of remotely sensed data has encouraged the development of new models or analysis procedures for the use of this information.

Billingsley, F. C.↗
NASA NTRS2007

ISPATOM: A Generic Real-Time Data Processing Tool Without Programming

Information Sharing Protocol Advanced Tool of Math (ISPATOM) is an application program allowing for the streamlined generation of comps, which subscribe to streams of incoming telemetry data, perform any necessary computations on the data, then send the data to other programs for display and/or further processing in NASA mission control centers. Heretofore, the development of comps was difficult, expensive, and time-consuming: Each comp was custom written manually, in a low-level computing language, by a programmer attempting to follow requirements of flight controllers. ISPATOM enables a flight controller who is not a programmer to write a comp by simply typing in one or more equation( s) at a command line or retrieving the equation(s) from a text file. ISPATOM then subscribes to the necessary input data, performs all of necessary computations, and sends out the results. It sends out new results whenever the input data change. The use of equations in ISPATOM is no more difficult than is entering equations in a spreadsheet. The time involved in developing a comp is thus limited to the time taken to decide on the necessary equations. Thus, ISPATOM is a real-time dynamic calculator.

Dershowitz, Adam↗