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Jee, J. R.

Publications and source records attributed to Jee, J. R..

4 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

GPS tracking of TOPEX/POSEIDON: Results and implications

A reduced dynamic filtering strategy that exploits the unique geometric strength of the Global Positioning System (GPS) to minimize the effects of force model errors has yielded orbit solutions for TOPEX/POSEIDON which appear accurate to better than 3 cm (1 sigma) in the radial component. Reduction of model error also reduces the geographic correlation of the orbit error. With a traditional dynamic approach, GPS yields radial orbit accuracies of 4-5 cm, comparable to the accuracy delivered by satellite laser ranging and the Doppler orbitography and radio positioning integrated by satellite (DORIS) tracking system. A portion of the dynamic orbit error is in the Joint Gravity Model-2 (JGM-2); GPS data from TOPEX/POSEIDON can readily reveal that error and have been used to improve the gravity model.

Bertiger, W. I.↗
NASA NTRS1993

A Look at a Day of Data from the Topex/Poseidon GPS Receiver

Launched on August 10, 1992, the Topex/Poseidon satellite is a joint project of NASA and the French space agency CNES. The pupose of this Earth orbiting altimetric satellite (for brevity, referred to as Topex) is to enable monitoring of the ocean topography to higher levels of accuracy than previously achieved.

robust regression spline graphics↗
NASA NTRS1987

Robust statistical methods for automated outlier detection

The computational challenge of automating outlier, or blunder point, detection in radio metric data requires the use of nonstandard statistical methods because the outliers have a deleterious effect on standard least squares methods. The particular nonstandard methods most applicable to the task are the robust statistical techniques that have undergone intense development since the 1960s. These new methods are by design more resistant to the effects of outliers than standard methods. Because the topic may be unfamiliar, a brief introduction to the philosophy and methods of robust statistics is presented. Then the application of these methods to the automated outlier detection problem is detailed for some specific examples encountered in practice.

Jee, J. R.↗