The COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) mission has GPS (Global Positioning System) radio occultation dual-band receivers onboard. The received signals slice through the ionosphere, layer by layer, in much the same way as peeling off the layers of an onion. In order to use GPS signals for ionospheric measurements, they must be edited, phase leveled, and the hardware biases removed. The leveling algorithm used for ground-based GPS receivers is inadequate for space-based receivers due to substantially different multipath characteristics.
A new leveling algorithm was developed utilizing GPS multipath error distributions to provide improved data quality. With improved confidence in ionospheric measurements, high-quality hardware biases may be estimated using assumptions about high-latitude ionospheric conditions.
Using the principle of maximum entropy for the code- and phase-based ionospheric observations, a new weighting scheme was developed for providing a weighted average of the difference of code- and phase-based combinations. The new weights are derived using GPS multipath characteristics. Hardware biases are computed under the assumption that the high-latitude ionosphere above a COSMIC satellite is relatively small (typically <1.0 TECU or total electron content units). Studies of the plasmasphere and ionosphere indicate this is a fair assumption. Therefore, the hardware bias can be assumed to be the remainder of the measured ionospheric delay for the upward-looking high-latitude observations.
The new leveling algorithm is the first to use explicitly the multipath information to determine the weighted mean, and thus the error in GPS leveling, of the difference between code- and phase-based ionospheric observables. It is shown that this new technique reduces the variance in the differences by about 0.4 TECU or more (equivalent to 0.14 ns delay) in the high-multipath environment of COSMIC. The bias estimation technique is shown to provide a stable and consistent hardware bias. Analysis of time series of the estimated biases indicates an improved repeatability and where large instabilities occur, these are likely due to real changes in the receiver hardware biases.
This work was done by Philip Stephens, Attila Komjathy, Brian D. Wilson, and Anthony J. Mannucci of Caltech for NASA’s Jet Propulsion Laboratory.
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