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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 73 records · Page 4
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NASA NTRS1995

Low Earth Orbit satellite traffic simulator

This paper describes a significant tool for Low Earth Orbit (LEO) capacity analysis, needed to support marketing, economic, and design analysis, known as a Satellite Traffic Simulator (STS). LEO satellites typically use multiple beams to help achieve the desired communication capacity, but the traffic demand in these beams in usually not uniform. Simulations of dynamic, average, and peak expected demand per beam is a very critical part of the marketing, economic, and design analysis necessary to field a viable LEO system. An STS is described in this paper which can simulate voice, data and FAX traffic carried by LEO satellite beams and Earth Station Gateways. It is applicable world-wide for any LEO satellite constellations operating over any regions. For aeronautical applications to LEO satellites. the anticipates aeronautical traffic (Erlangs for each hour of the day to be simulated) is prepared for geographically defined 'area targets' (each major operational region for the respective aircraft), and used as input to the STS. The STS was designed by Constellations Communications Inc. (CCI) and E-Systems for usage in Brazil in accordance with an ESCA/INPE Statement Of Work, and developed by Analytical Graphics Inc. (AGI) to execute on top of its Satellite Tool Kit (STK) commercial software. The STS simulates constellations of LEO satellite orbits, with input of traffic intensity (Erlangs) for each hour of the day generated from area targets (such as Brazilian States). accumulated in custom LEO satellite beams, and then accumulated in Earth Station Gateways. The STS is a very general simulator which can accommodate: many forms of orbital element and Walker Constellation input; simple beams or any user defined custom beams; and any location of Gateways. The paper describes some of these features, including Manual Mode dynamic graphical display of communication links, to illustrate which Gateway links are accessible and which links are not, at each 'step' of the satellite orbit. In the two Performance Modes, either Channel capacity or Grade Of Service (GOS) for objects (Satellite beams, Gateways, and an entire satellite) are computed respectively by standard traffic table capacity lookup and blocking probability equations. GOS can be input, with number of channels calculated, or number of channels can be input, with GOS calculated. Also described are some of the STS Test Procedure approach and results. AGI plans to make the STS features available through their normal commercial STK products. E-Systems is a co-developer, tester, and user of the STS. The Test Procedure for the STS was prepared by E-Systems, as an independent tester for CCI, to support the CCI delivery of the STS to ESCA, for their customer INPE.

Hoelzel, John↗
NASA NTRS1960

Determination of Satellite Orbits from Radar Data

An optimum method for determining satellite orbits from radar data is presented in this report. Offering a good combination of speed and accuracy, the method makes use of orbit inclination and orbit elements in the plane, and proceeds with a differential correction of the orbit elements. Rapid, accurate methods of computing orbit elements are required to predict satellite positions for acquisition by other radars at points later along the orbit. In some cases the data are limited to a single pass over the observing station. The dynamical method is described in detail, and its accuracy is compared with those of two other methods: the purely geometrical, and the least-squares geometrical. By this optimum method the computing time, including the differential correction time, is 1 minute. Without differential correction, the rough determination takes from 10 to 20 seconds with approximately 5 miles positional uncertainty.

Cahill, W. F.↗
NASA NTRS1995

Space and time aliasing structure is monthly mean polar-orbiting satellite data

Monthly mean wind fields from the European Remote Sensing Satellite (ERS1) scatterometer are presented. A banded structure which resembles the satellite subtrack is clearly and consistently apparent in the isotachs as well as the u and v components of the routinely produced fields. The structure also appears in the means of data from other polar-orbiting satellites and instruments. An experiment is designed to trace the cause of the banded structure. The European Centre for Medium-Range Weather Forecast (ECMWF) gridded surface wind analyses are used as a control set. These analyses are also sampled with the ERS1 temporal-spatial samplig pattern to form a simulated scatterometer wind set. Both sets are used to create monthly averages. The banded structures appear in the monthly mean simulated data but do not appear in the control set. It is concluded that the source of the banded structure lies in the spatial and temporal sampling of the polar-orbiting satellite which results in undersampling. The problem involves multiple timescales and space scales, oversampling and under-sampling in space, aliasing in the time and space domains, and preferentially sampled variability. It is shown that commonly used spatial smoothers (or filters), while producing visually pleasing results, also significantly bias the true mean. A three-dimensional spatial-temporal interpolator is designed and used to determine the mean field. It is found to produce satisfactory monthly means from both simulated and real ERS1 data. The implications to climate studies involving polar-orbiting satellite data are discussed.

Zeng, Lixin↗
NASA NTRS1973

Investigation of halo satellite orbit control

Calculations are given for Halo satellite orbit control. Previous truncated analytical descriptions were limited and lead to an acceleration error averaging about .000001, which is the 'cost' of a very tight control to the nominal path. The stationkeeping problem is posed so as to permit a looser, optimal three-axis control.

Breakwell, J. V.↗
NASA NTRS1984

History of on-orbit satellite fragmentations

The causes of on-orbit fragmentations are varied and may be intentional or accidental. The cause of many fragmentations remains unknown. While a few cases are currently under investigation as on-orbit collision candidates, man is directly responsible for the vast majority of artificial debris polluting the near-Earth space environment. It should be emphasized that the number of fragments listed with each event in this document represent only those debris officially cataloged by NORAD. Each known on-orbit satellite fragementation is described within this document in module format. Also listed are pertinent characteristics of each fragmentation event. Comments regarding the nature of the satellite and additional details of the events are given.

Johnson, N. L.↗
NASA NTRS1992

Generalized probability model for calculation of interference to the Deep Space Network due to circularly Earth-orbiting satellites

The probability of exceeding interference power levels and the duration of interference at the Deep Space Network (DSN) antenna is calculated parametrically when the state vector of an Earth-orbiting satellite over the DSN station view area is not known. A conditional probability distribution function is derived, transformed, and then convolved with the interference signal uncertainties to yield the probability distribution of interference at any given instant during the orbiter's mission period. The analysis is applicable to orbiting satellites having circular orbits with known altitude and inclination angle.

Ruggier, C. J.↗
NASA NTRS1991

System simulation on advanced earth-orbital satellite systems

A system simulation capability for advanced earth-orbital satellite systems (AEUSS) is presented. It is based on analytical assessment capabilities formulated for generic satellite systems in earth-orbital missions. The resulting AEOSS is a custom tailored software coded within the framework of the Macintosh version of the Acius relational database program, 4th Dimension. It projects the required power, weight, and cost for a satellite system. These key elements are computed on the component and subsystem levels, and then the system level. Selected performance analyses for essential components and subsystems are included.

Lee, Hwa-Ping↗
NASA NTRS2023

Implementing a VLBI Time Delay Model for Earth-orbiting Satellites: Partial derivatives and Verification

This document describes the partial derivatives for commonly estimated parameters including antenna positions, satellite position, and satellite velocity from a near-field VLBI delay model for Earth-orbiting satellites. This model was presented in the Journal of Geodesy by Jaron & Nothnagel (2019). In this context, we present a streamlined version of the near-field VLBI delay. From this simplified model, we deduce a delay rate expression and calculate partial derivatives, maintaining only the terms with significant impact on the computed derivatives’ magnitude. To verify the accuracy of the simplified model and the partial derivatives computed from it, we have created a simple simulation in Matlab of an Earth-orbiting satellite at the altitude of a typical Global Navigation Satellite Systems (GNSS) satellite. From this simulation, we compare the simplified and original delays, and we verify the magnitude and direction of the partial derivatives against the numerically computed derivatives from the original delay model. The partial derivatives and simplified VLBI delay model detailed here are implemented in Fortran in the open-source library VTD.

Geodesy↗
NASA NTRS1985

Automated testing of low-orbiting satellites

This paper describes the hardware, software, and test procedures implemented at a ground station for the automated testing of a polar-orbiting satellite in a low orbit. The satellite passes during the testing were characterized by short visibility times of no more than 18 min and by high rates of change of the link parameters as seen by the ground station. The advantages of automated testing became apparent for these dynamic conditions. Under computer control throughout the pass, programmable test instruments were commanded and data was collected and stored. Data reduction was performed after the pass.

Barbiere, D.↗