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Results for “IONOSPHERIC ELECTRON DENSITY”

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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Higher-order ionospheric effects on the GPS observables and means of modeling them

Based on relaistic modeling of the electron density of the ionosphere and using a dipole moment approximation for the earth magnetic field, we are able to estimate the effect of the ionosphere on the Global Positioning Systems (GPS) signal for a ground user. The lowest-order (1/f(exp 2)) effect, which is of the order of .1 - 30 meters of zenith group delay, is subtracted out by forming a linear combination of the dual frequencies of the GPS signal. One is left with second- (1/f(exp 3)) and third-order (1/f(exp 4)) effects which are estimated typically to be approximately 0 - 2 cm, and approximately 0 - 2 mm at zenith respectively, depending on the time of day, time of year, the solar cycle and the relative geometry of the magnetic field and the line of sight. Given the total electron content along a line of sight, we derive an approximation to the second-order term which is accurate to approximately 90% within the magnetic dipole moment model; this approximation can be used to reduce the second-order term to the millimeter level, thus potentially improving precise positioning in space and on the ground. The induced group delay, or phase advance, due to second- and third-order effects are examined for two ground receivers located at equatorial and mid-latitude regions tracking several GPS satellites.

Bassiri, Sassan↗
NASA NTRS1993

Higher-Order Ionospheric Effects on the GPS Observables and Means of Modeling Them

Based on realistic modeling of the electron density of the ionosphere and using a dipole moment approximation for the earth magnetic field, we are able to estimate the effect of the ionosphere on the GPS signal for a ground user. Our study implies reduction of the second-order ionospheric error to less than 2 mm on the average and, therefore, potential improvement of orbit determination and baseline solutions.

ionosphere↗
NASA NTRS2005

Processing GPS Occultation Data To Characterize Atmosphere

GOAS [Global Positioning System (GPS) Occultation Analysis System] is a computer program that accepts signal-occultation data from GPS receivers aboard low-Earth-orbiting satellites and processes the data to characterize the terrestrial atmosphere and, in somewhat less comprehensive fashion, the ionosphere. GOAS is very robust and can be run in an unattended semi-operational processing mode. It features sophisticated retrieval algorithms that utilize the amplitudes and phases of the GPS signals. It incorporates a module that, using an assumed atmospheric refractivity profile, simulates the effects of the retrieval processing system, including the GPS receiver. GOAS utilizes the GIPSY software for precise determination of orbits as needed for calibration. The GOAS output for the Earth s troposphere and mid-to-lower stratosphere consists of high-resolution (<1 km) profiles of density, temperature, pressure, atmospheric refractivity, bending angles of signals, and water-vapor content versus altitude from the Earth s surface to an altitude of 30 km. The GOAS output for the ionosphere consists of electron-density profiles from an altitude of about 50 km to the altitude of a satellite, plus parameters related to the rapidly varying structure of the electron density, particularly in the E layer of the ionosphere.

Hajj, George↗
NASA NTRS1983

The ionosphere of Venus - Observations and their interpretation

The implications of Soviet and U.S. observations of the Venus ionosphere's density, temperature, composition, motion, and magnetic structure are discussed, in view of the strong influence exerted on nearly all ionospheric parameters by the solar wind. The IMF conveys solar wind pressure to the ionosphere, compressing, accelerating, heating and removing plasma, forming the ionopause and inducing a nightward convection of plasma. Within the ionosphere, the main electron density peak is at an altitude of about 140 km on the day side, and is believed to be formed by local production and loss analogous to the earth's E region. Throughout most of the ionosphere, the nightward ion flow is primarily driven by the day-to-night pressure gradient, and electron precipitation also contributes to the nightside ionization. The lower atmosphere is dominated by O2(+), except at the lowest altitudes at night, where NO(+) and CO2(+) become significant ions.

Brace, L. H.↗
NASA NTRS2022

Extreme Poleward Expanding Super Plasma Bubbles Over Asia-Pacific Region Triggered By Tonga Volcano Eruption During the Recovery-Phase of Geomagnetic Storm

The Tonga volcano eruption of 15 January 2022 unleashed a variety of atmospheric perturbations, coinciding with the recovery-phase of a geomagnetic storm. The ensuing thermospheric variations created rare display of extreme poleward-expanding conjugate plasma bubbles seen in the rate of total electron content index over 100–150°E, reaching ∼40°N geographic latitude. This is associated with fluctuations in FORMOSAT-7/COSMIC-2 (F7/C2) ion-density measurements and spread-F in ionograms. Preceding to this, an unusually strong pre-reversal enhancement (PRE) occurred in the global ionospheric specification (GIS) electron density profiles derived from F7/C2 observations. The GIS also revealed a decrease of equatorial ionization anomaly (EIA) crest density due to the storm impact. Reduced E-region conductivity by volcano-induced waves and enhanced F-region wind, further accelerated by reduced ion-drag over the EIA, apparently intensified the PRE. Accompanied with the strong PRE, volcano-induced seed perturbations triggered the super plasma bubble activity.

P. K. Rajesh↗
NASA NTRSDate not supplied

Distributed Spacecraft Autonomy - Development of Swarm Autonomy Capability and Scalability for Spacecraft

The Distributed Spacecraft Autonomy project is developing a suite of software tools that enable an operator to command and receive data from a swarm as a single entity, enable a swarm to autonomously coordinate its actions via distributed decision making and reactive closed-loop control, and model swarm behavior in the presence of anomalies or failures. Our use case is the mapping of the electron density of the ionosphere using radio tomography by coordinating the selection of appropriate GPS channels, and by recording Total Electron Count (TEC) measurements. DSA will be demonstrated onboard the NASA Ames Starling mission – a swarm of four small, LEO spacecraft, scheduled to launch in 2021. We will also perform a ground demonstration with simulated and hardware-in-the-loop elements, to validate the tools for controlling swarms of up to 100 assets. The capability to communicate autonomously between the swarm satellites is demonstrated via a sophisticated simulation architecture. Historical Plasmasphere TEC data obtained via dual-band Novatel GPS Receivers are utilized as a representative input dataset for the swarm. The representative TEC data and GPS satellite observability information is fed to the autonomous software package in place of a true real-time ground data collection process. The swarm satellites actively share status updates amongst one another and utilize multi-agent decision making to optimally identify regions of interest in the TEC distribution. The software, aware of the bandwidth limitations of the swarm satellites, prioritizes explorative measurements, which define the range of observability for the satellites, as well as exploitative measurements, which focus on maximizing the observance potential of regions with prolonged, elevated TEC density. The science of this study can ultimately be used to determine the dynamics and coupling of Earth’s magnetosphere, ionosphere, and atmosphere and their response to solar and terrestrial inputs. The findings can be applied to the imaging of critical, transient phenomena in the magnetosphere in later missions. Meanwhile, the swarm autonomy capabilities have far reaching potential in future satellite missions. As an experimental demonstration of the autonomous capabilities of the network, a message is first printed within a core Flight Executive (cFE) application. Two cFE applications that communicate with one another within the same core Flight System (cFS) are shown. Communication between mission applications on the internal cFE bus is extended to utilize Data Distribution Service (DDS) for vehicle-to-vehicle networking. The DDS middleware provides reliable delivery, routing, and topic subscription features over User Datagram Protocol (UDP). Leveraging Linux containerization, a networked set of satellite instances are generated by script to simulate swarm behavior. Swarm commanding and synchronization through the network is demonstrated under various topologies and data-loss conditions. Finally, autonomous swarm scalability from 2 satellites to 100 satellites is shown.

Distributed Autonomy↗
NASA NTRS2020

Distributed Spacecraft Autonomy (DSA): Development of Swarm Autonomy Capability and Scalability for Spacecraft

The Distributed Spacecraft Autonomy project is developing a suite of software tools that enable an operator to command and receive data from a swarm as a single entity, enable a swarm to autonomously coordinate its actions via distributed decision making and reactive closed-loop control, and model swarm behavior in the presence of anomalies or failures. Our use case is the mapping of the electron density of the ionosphere using radio tomography by coordinating the selection of appropriate GPS channels, and by recording Total Electron Count (TEC)measurements. DSA will be demonstrated on board the NASA Ames Starling mission a swarm of four small, LEO spacecraft, scheduled to launch in 2021. We will also perform a ground demonstration with simulated and hardware-in-the-loop elements, to validate the tools for controlling swarms of up to 100 assets.The capability to communicate autonomously between the swarm satellites is demonstrated via a sophisticated simulation architecture. Historical Plasma sphere TEC data obtained via dual-band Novatel GPS Receivers are utilized as a representative input data set for the swarm. The representative TEC data and GPS satellite observability information is fed to the autonomous software package in place of a true real-time ground data collection process. The swarm satellites actively share status updates amongst one another and utilize multi-agent decision making to optimally identify regions of interest in the TEC distribution. The software,aware of the bandwidth limitations of the swarm satellites, prioritizes explorative measurements,which define the range of observability for the satellites, as well as exploitative measurements,which focus on maximizing the observance potential of regions with prolonged, elevated TEC density. The science of this study can ultimately be used to determine the dynamics and coupling of Earth's magnetosphere, ionosphere, and atmosphere and their response to solar and terrestrial inputs. The findings can be applied to the imaging of critical, transient phenomena in the magnetosphere in later missions. Meanwhile, the swarm autonomy capabilities have far reaching potential in future satellite missions.As an experimental demonstration of the autonomous capabilities of the network, a message is first printed within a core Flight Executive (cFE) application. Two cFE applications that communicate with one another within the same core Flight System (cFS) are shown.Communication between mission applications on the internal cFE bus is extended to utilize Data Distribution Service (DDS) for vehicle-to-vehicle networking. The DDS middle ware provides reliable delivery, routing, and topic subscription features over User Data gram Protocol (UDP).Leveraging Linux containerization, a networked set of satellite instances are generated by script to simulate swarm behavior. Swarm commanding and synchronization through the network is demonstrated under various topologies and data-loss conditions. Finally, autonomous swarms calability from 2 satellites to 100 satellites is shown.

Fugate, Jason↗