Robust Differential GPS Processing Using, Jupiter: Algorithms and Results

Yang Gao, James McLellan, and M.A. Forkheim

Abstract:	For high precision differential GPS positioning and navigation, use of robust data processing and management algorithms are critical in order to derive highly accurate and reliable positioning solutions. Described in this paper are various algorithms designed for and implemented into a commercial software package for robust differential GPS processing. The focus of the paper has been on the following three components: robust quality control, parameter estimation and data management. The quality control procedure utilizes a wide variety of statistical measures for fault detection and identification. State-of-the-art reliability analysis is also applied to ensure the robustness of the statistical test process. Parameter estimation employs a new Kalman filtering algorithm which allows rigorous processing of GPS data correlated over time and ensures the derived solution reaches the highest accuracy possible. The user- friendly data management provides options for users to perform further data analysis and to derive a robust positioning solution. Numerical results and analysis based on various data sets are presented to demonstrate the performance of the designed data processing schemes.
Published in:	Proceedings of the 1996 National Technical Meeting of The Institute of Navigation January 22 - 24, 1996 Loews Santa Monica Hotel Santa Monica, CA
Pages:	587 - 595
Cite this article:	Gao, Yang, McLellan, James, Forkheim, M.A., "Robust Differential GPS Processing Using, Jupiter: Algorithms and Results," Proceedings of the 1996 National Technical Meeting of The Institute of Navigation, Santa Monica, CA, January 1996, pp. 587-595.
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