A computer program uses data from more than 1,000 Global Positioning System (GPS) receivers in an Internetaccessible global network to generate daily estimates of the global distribution of vertical total electron content (VTEC) of the ionosphere. This program supersedes an older program capable of processing readings from only about 200 GPS receivers. This program downloads the data via the Internet, then processes the data in three stages. In the first stage, raw data from a global subnetwork of about 200 receivers are preprocessed, station by station, in a Kalman-filterbased least-squares estimation scheme that estimates satellite and receiver differential biases for these receivers and for satellites. In the second stage, an observation equation that incorporates the results from the first stage and the raw data from the remaining 800 receivers is solved to obtain the differential biases for these receivers. The only remaining error sources for which an account cannot be given are multipath and receiver noise contributions. The third stage is a postprocessing stage in which all the processed data are combined and used to generate new data products, including receiver differential biases and global and regional VTEC maps and animations.
This program was written by Attila Komjathy and Anthony Mannucci of Caltech for NASA's Jet Propulsion Laboratory. For further information, access the Technical Support Package (TSP) free on-line at www.techbriefs.com/tsp under the Software category.
This software is available for commercial licensing. Please contact Karina Edmonds of the California Institute of Technology at (818) 393-2827. Refer to NPO-41612.
This Brief includes a Technical Support Package (TSP).
Estimating Total Electron Content Using 1,000+ GPS Receivers
(reference NPO-41612) is currently available for download from the TSP library.
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Overview
The document outlines the development of a new algorithm designed to estimate global vertical total electron content (VTEC) using over 1,000 ground-based GPS receivers. This innovation addresses the limitations of previous algorithms, which could only process data from about 200 receivers at a time. The new algorithm enables the simultaneous processing of data from all available GPS receivers, significantly enhancing the ability to monitor the spatial and temporal variability of the global ionosphere.
The algorithm consists of three main components. The first part involves estimating precise satellite and receiver interfrequency biases through a Kalman-filter based least-squares estimation scheme, utilizing data from approximately 200 GPS receivers worldwide. This step pre-processes raw GPS data station by station, leveling the phase ionospheric observable to code-based measurements. The output serves as the background ionosphere for the subsequent processing steps.
In the second part, the algorithm uses the ionospheric data from the initial 200 stations to correct the remaining 800 stations for ionospheric contributions. By forming a unique observation equation, the algorithm effectively removes unknowns from the system of normal equations, allowing for the direct estimation of receiver differential biases across the global network. The only remaining error sources are multipath and receiver noise contributions.
The third part of the process involves post-processing the combined data to generate new products for the scientific and engineering community. These products include global and regional vertical electron content maps and satellite and receiver differential biases. Since its implementation in October 2004, the software package has been running continuously, generating daily TEC movies that have garnered significant attention due to the unprecedented volume of GPS data processed in an automated manner.
The document also highlights the potential applications of this technology in studying the effects of solar and geomagnetic storms on the global ionosphere, which is crucial for NASA's manned and robotic missions. The ability to monitor ionospheric conditions can help mitigate risks associated with energetic solar particles that may impact space exploration missions.
Overall, this innovative algorithm represents a significant advancement in the field of ionospheric research, providing valuable insights and tools for the scientific community and enhancing our understanding of space weather phenomena.
