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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Precise GPS ephemerides from DMA and NGS tested by time transfer

It was shown that the use of the Defense Mapping Agency's (DMA) precise ephemerides brings a significant improvement to the accuracy of GPS time transfer. At present a new set of precise ephemerides produced by the National Geodetic Survey (NGS) has been made available to the timing community. This study demonstrates that both types of precise ephemerides improve long-distance GPS time transfer and remove the effects of Selective Availability (SA) degradation of broadcast ephemerides. The issue of overcoming SA is also discussed in terms of the routine availability of precise ephemerides.

W. Lewandowski↗
NASA NTRS2017

Space Telescope and Optical Reverberation Mapping Project. VI. Reverberating Disk Models for NGS 5548

We conduct a multi-wavelength continuum variability study of the Seyfert 1 galaxy NGC 5548 to investigate the temperature structure of its accretion disk. The 19 overlapping continuum light curves (1158 Angstrom to 9157 Angstrom) combine simultaneous Hubble Space Telescope, Swift, and ground-based observations over a 180 day period from 2014 January to July. Light-curve variability is interpreted as the reverberation response of the accretion disk to irradiation by a central time-varying point source. Our model yields the disk inclination i = 36deg +/- 10deg, temperature T(sub 1) = (44+/-6) times 10 (exp 3)K at 1 light day from the black hole, and a temperature radius slope (T proportional to r (exp -alpha)) of alpha = 0.99 +/- 0.03. We also infer the driving light curve and find that it correlates poorly with both the hard and soft X-ray light curves, suggesting that the X-rays alone may not drive the ultraviolet and optical variability over the observing period. We also decompose the light curves into bright, faint, and mean accretion-disk spectra. These spectra lie below that expected for a standard blackbody accretion disk accreting at L/L(sub Edd) = 0.1.

accretion↗
NASA NTRS1991

GPS orbit determination at the National Geodetic Survey

The National Geodetic Survey (NGS) independently generates precise ephemerides for all available Global Positioning System (GPS) satellites. Beginning in 1991, these ephemerides were produced from double-differenced phase observations solely from the Cooperative International GPS Network (CIGNET) tracking sites. The double-difference technique combines simultaneous observations of two satellites from two ground stations effectively eliminating satellite and ground receiver clock errors, and the Selective Availability (S/A) signal degradation currently in effect. CIGNET is a global GPS tracking network whose primary purpose is to provide data for orbit production. The CIGNET data are collected daily at NGS and are available to the public. Each ephemeris covers a single week and is available within one month after the data were taken. Verification is by baseline repeatability and direct comparison with other ephemerides. Typically, an ephemeris is accurate at a few parts in 10(exp 7). This corresponds to a 10 meter error in the reported satellite positions. NGS is actively investigating methods to improve the accuracy of its orbits, the ultimate goal being one part in 10(exp 8) or better. The ephemerides are generally available to the public through the Coast Guard GPS Information Center or directly from NGS through the Geodetic Information Service. An overview of the techniques and software used in orbit generation will be given, the current status of CIGNET will be described, and a summary of the ephemeris verification results will be presented.

M. S. Schenewerk↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), has typically limited machine learning (ML) in space studies and further study of radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNAseq) data from 6 mouse liver GeneLab datasets (GLDS) with a total of 113 spaceflight and ground-control samples to determine top features relevant to spaceflight including the effect of radiation exposure. Data was normalized within each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. The top MRMR features were used to predict spaceflight vs. ground-control samples using a Random Forest (RF) classifier with 5-fold cross validation (CV). The ML-based gene sets were further compared against differential gene expression results from individual GLDS. CV training using the top 100 MRMR genes show averages of 86% accuracy and 0.95 AUC value on the validation set over 5 folds (Figure 1A). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 811 or 68 DEGs overlapping between at least 2 or 3 studies, respectively (Figure 1B). Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism. Set analysis between the MRMR features and the DEGs showed 60 or 8 genes overlapping with at least 1 or 2 studies, respectively. MRMR feature selection and ensemble ML methods (e.g. RF) improve performance relative to a Naïve Bayes classifier when NGS data sets are analyzed. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise ratio. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from RNASeq analysis. Non-intersecting sets introduce opportunity to explore spaceflight relevant genes and implementing ML methods across existing NGS datasets may overcome sample size limitations. ML coupled with existing analytical methods enhances understanding of disease by revealing common underlying pathways across datasets.

Machine Learning↗
NASA NTRS1992

The ACTS NASA Ground Station/Master Control Station

Two of the major components of the ACTS Ground Segment are the NASA Ground Station (NGS) and the Master Control Station (MCS), colocated at the NASA Lewis Research Center. Essentially, the NGS provides the communications links by which the MCS performs its various network control and monitoring functions. The NGS also provides telecommunications links capable of transmission/reception of up to approximately 70 Mbit/s of digital telephonic traffic. Operating as a system, the entire complex of equipment is referred to as the NGS/MCS. This paper provides an 'as-built' description of the NGS/MCS as a system.

Meadows, David N.↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRS1996

Compilation and Analysis of 20 and 30 GHz Rain Fade Events at the ACTS NASA Ground Station: Statistics and Model Assessment

The purpose of the propagation studies within the ACTS Project Office is to acquire 20 and 30 GHz rain fade statistics using the ACTS beacon links received at the NGS (NASA Ground Station) in Cleveland. Other than the raw, statistically unprocessed rain fade events that occur in real time, relevant rain fade statistics derived from such events are the cumulative rain fade statistics as well as fade duration statistics (beyond given fade thresholds) over monthly and yearly time intervals. Concurrent with the data logging exercise, monthly maximum rainfall levels recorded at the US Weather Service at Hopkins Airport are appended to the database to facilitate comparison of observed fade statistics with those predicted by the ACTS Rain Attenuation Model. Also, the raw fade data will be in a format, complete with documentation, for use by other investigators who require realistic fade event evolution in time for simulation purposes or further analysis for comparisons with other rain fade prediction models, etc. The raw time series data from the 20 and 30 GHz beacon signals is purged of non relevant data intervals where no rain fading has occurred. All other data intervals which contain rain fade events are archived with the accompanying time stamps. The definition of just what constitutes a rain fade event will be discussed later. The archived data serves two purposes. First, all rain fade event data is recombined into a contiguous data series every month and every year; this will represent an uninterrupted record of the actual (i.e., not statistically processed) temporal evolution of rain fade at 20 and 30 GHz at the location of the NGS. The second purpose of the data in such a format is to enable a statistical analysis of prevailing propagation parameters such as cumulative distributions of attenuation on a monthly and yearly basis as well as fade duration probabilities below given fade thresholds, also on a monthly and yearly basis. In addition, various subsidiary statistics such as attenuation rate probabilities are derived. The purged raw rain fade data as well as the results of the analyzed data will be made available for use by parties in the private sector upon their request. The process which will be followed in this dissemination is outlined in this paper.

Manning, Robert M.↗
NASA NTRS2000

ACTS Ka-Band Earth Stations: Technology, Performance, and Lessons Learned

The Advanced Communications Technology Satellite (ACTS) Project invested heavily in prototype Ka-band satellite ground terminals to conduct an experiments program with the ACTS satellite. The ACTS experiment's program proposed to validate Ka-band satellite and ground station technology. demonstrate future telecommunication services. demonstrate commercial viability and market acceptability of these new services, evaluate system networking and processing technology, and characterize Ka-band propagation effects, including development of techniques to mitigate signal fading. This paper will present a summary of the fixed ground terminals developed by the NASA Glenn Research Center and its industry partners, emphasizing the technology and performance of the terminals (Part 1) and the lessons learned throughout their six year operation including the inclined orbit phase of operations (Full Report). An overview of the Ka-band technology and components developed for the ACTS ground stations is presented. Next. the performance of the ground station technology and its evolution during the ACTS campaign are discussed to illustrate the technical tradeoffs made during the program and highlight technical advances by industry to support the ACTS experiments program and terminal operations. Finally. lessons learned during development and operation of the user terminals are discussed for consideration of commercial adoption into future Ka-band systems. The fixed ground stations used for experiments by government, academic, and commercial entities used reflector based offset-fed antenna systems ranging in size from 0.35m to 3.4m antenna diameter. Gateway earth stations included two systems, referred to as the NASA Ground Station (NGS) and the Link Evaluation Terminal (LET). The NGS provides tracking, telemetry, and control (TT&C) and Time Division Multiple Access (TDMA) network control functions. The LET supports technology verification and high data rate experiments. The ground stations successfully demonstrated many services and applications at Ka-band in three different modes of operation: circuit switched TDMA using the satellite on-board processor, satellite switched SS-TDMA applications using the on-board Microwave Switch Matrix (MSM), and conventional transponder (bent-pipe) operation. Data rates ranged from 4.8 kbps up to 622 Mbps. Experiments included: 1) low rate (4.8- 1 00's kbps) remote data acquisition and control using small earth stations, 2) moderate rate (1-45 Mbps) experiments included full duplex voice and video conferencing and both full duplex and asymmetric data rate protocol and network evaluation using mid-size ground stations, and 3) link characterization experiments and high data rate (155-622 Mbps) terrestrial and satellite interoperability application experiments conducted by a consortium of experimenters using the large transportable ground stations.

Reinhart, Richard C.↗
NASA NTRS1988

Space Age Archaeology

In 1985, the Egyptian Antiques Organization (EAO) asked Dr. Farouk El-Baz whether it would be possible to examine and sample the second chamber of the subterranean chamber carved in the bedrock near the Great Pyramid of Khufu in Giza, Egypt, without admitting people, air or contaminants. He felt it could by applying space technology to the task. The initial contact led to a two year project which he organized and headed a team, co-sponsored by EAO and the National Geographic Society (NGS), to apply space technology in an effort to examine and photograph the Giza Chamber. The NGS photographic division modified and tested a remotely controlled video system and a 35-millimeter camera, and developed a lighting system that would not elevate the chamber temperature. Still needed was a drill to cut through the limestone cap without using lubricants or cooling fluids that might contaminate the chamber, and an airlock that would admit the drill shaft and photo equipment but not the air. Bob Moores from Black & Decker Corporation tailored a new drill to the Giza exploration. The drill bit broke through into the chamber at a depth of 63 inches, a stainless steel tube was lowered through the airlock to take samples of the chamber air at several levels. The video camera sent images from the chamber revealing that there was a disassembled royal boat that had been there.

Source record↗
NASA NTRS2026

Multi-Agent Swarm State of the Art Report

The Next-Generation Multi-Agent Swarm (NGS) Study conducted by NASA’s Ames Research Center for NASA’s Space Technology Mission Directorate (STMD) will develop a comprehensive understanding of emerging multi-agent swarm capabilities. The study aims to identify existing swarm capabilities and asses their potential for persistent lunar space situational awareness, surface monitoring, and distributed autonomy demonstrations. A key objective is to inform the design of a next-generation multi-agent swarm that can perform autonomous distributed remote sensing, position, navigation, and timing (PNT) services, automated deployment that leverages autonomy, edge computing, and interoperable networking to enable cooperative operations without the need for immediate human operation. This study will address specific shortfalls identified by STMD, including intelligent multi-agent constellations, autonomy, edge computation, position, navigation, and timing for small spacecraft, small spacecraft propulsion, and space situational awareness (1625, 1438, 1433, 1557, 1431, 1430, 1589). The NASA Ames Mission Design Center (MDC) will provide subject matter expertise to support systems engineering trades, while experts in autonomy and spacecraft swarms in NASA’s Intelligent Systems Division will lead the study and focus on identifying emerging next-generation swarm capabilities. The study objectives include: capturing the current state-of-the-art for multi-agent swarm capabilities, evaluating technologies and creating technology roadmaps, and developing at least one new technology demonstration mission concept. This initial NGS study report surveys the current state of the art in technology areas relevant for the next-generation multi-agent swarm design. Our primary focus is on surveying relevant deployed space systems1, supplemented with selective analysis of relevant proposed missions and technology developments that have yet to fly.

agent↗
NASA NTRS1979

Observations of far-infrared fine structure lines: o III88.35 micrometer and oI 63.2 micrometer

Observations of the O III 88.35 micrometer line and the O I63.2 micrometer were made with a far infrared spectrometer. The sources M17, NGC 7538, and W51 were mapped in the O III line with 1 arc minute resolution and the emission is found to be quite widespread. In all cases the peak of the emission coincides with the maximum radio continuum. The far infrared continuum was mapped simultaneously and in M17, NGC 7538, and W51 the continuum peak is found to be distinct from the center of ionization. The O III line was also detected in W3, W49, and in a number of positions in the Orion nebula. Upper limits were obtained on NGS 7027, NGC 6572, DR21, G29.9-0.0 and M82. The 63.2 micrometer O I line was detected in M17, M42, and marginally in DR21. A partial map of M42 in this line shows that most of the emission observed arises from the Trapezium and from the bright optical bar to the southeast.

Storey, J. W. V.↗
NASA NTRS1980

Red and nebulous objects in dark clouds - A survey

A search on the NGS-PO Sky Survey photographs has revealed 150 interesting nebulous and/or red objects, mostly lying in dark clouds and not previously catalogued. Spectral classifications are presented for 55 objects. These indicate a small number of new members of the class of Herbig-Haro objects, a significant number of new T Tauri stars, and a few emission-line hot stars. It is argued that hot, high-mass stars form preferentially in the dense cores of dark clouds. The possible symbiosis of high and low mass stars is considered. A new morphology class is defined for cometary nebulae, in which a star lies on the periphery of a nebulous ring.

Cohen, M.↗
NASA NTRS1983

Crustal dynamics project session 4 validation and intercomparison experiments 1979-1980 report

As part of the Crustal Dynamics Project, an experiment was performed to verify the ability of Satellite Laser Ranging (SLR), Very Long Baseline interferometry (VLBI) and Doppler Satellite Positioning System (Doppler) techniques to estimate the baseline distances between several locations. The Goddard Space Flight Center (GSFC) lasers were in operation at all five sites available to them. The ten baselines involved were analyzed using monthly orbits and various methods of selecting data. The standard deviation of the monthly SLR baseline lengths was at the 7 cm level. The GSFC VLBI (Mark III) data was obtained during three separate experiments. November 1979 at Haystack and Owens Valley, and April and July 1980 at Haystack, Owens Valley, and Fort Davis. Repeatability of the VLBI in determining baseline lengths was calculated to be at the 2 cm level. Jet Propulsion Laboratory (JPL) VLBI (Mark II) data was acquired on the Owens Valley to Goldstone baseline on ten occasions between August 1979 and November 1980. The repeatability of these baseline length determinations was calculated to be at the 5 cm level. National Geodetic Survey (NGS) Doppler data was acquired at all five sites in January 1980. Repeatability of the Doppler determined baseline lengths results were calculated at approximately 30 cm. An intercomparison between baseline distances and associated parameters was made utilizing SLR, VLBI, and Doppler results on all available baselines. The VLBI and SLR length determinations were compared on four baselines with a resultant mean difference of -1 cm and a maximum difference of 12 cm. The SLR and Doppler length determinations were compared on ten baselines with a resultant mean difference of about 30 cm and a maximum difference of about 60 cm. The VLBI and Doppler lengths from seven baselines showed a resultant mean difference of about 30 cm and maximum difference of about 1 meter. The intercomparison of baseline orientation parameters were consistent with past analysis.

Liebrecht, P.↗
NASA NTRS1988

The celestial reference frame defined by VLBI

VLBI currently produces the most accurate positions of celestial objects. From 1979 to 1987, 114 extragalactic radio sources have been observed with dual-frequency Mark III VLBI as part of the NASA Crustal Dynamics Project and the NGS POLARIS/IRIS program. The formal statistical errors of conventional celestial coordinates are as small as 0.3 milliarcseconds. The fundamental quantity measured by VLBI is the arc length between radio sources. Thus, it is suggested that VLBI be used to establish a coordinate reference frame based solely on radio positions, and that this system not necessarily be coupled to right ascension and declination.

Ma, C.↗
NASA NTRS1990

GPS-based orbit determination and point positioning under selective availability

Selective availability (SA) degrades the positioning accuracy for nondifferential users of the GPS Standard Positioning Service (SPS). The often quoted SPS accuracy available under normal conditions is 100 m 2DRMS. In the absence of more specific information, many prospective SPS users adopt the 100 m value in their planning, which exaggerates the error in many cases. SA error is examined for point positioning and dynamic orbit determination for an orbiting user. To minimize SA error, nondifferential users have several options: expand their field of view; observe as many GPS satellites as possible; smooth the error over time; and employ precise GPS ephemerides computed independently, as by NASA and the NGS, rather than the broadcast ephemeris. Simulations show that 3D point position error can be kept to 30 m, and this can be smoothed to 3 m in a few hours.

Bar-Sever, Yoaz E.↗