Differential Integer Ambiguity Resolution with Gaussian a Priori Knowledge and Kalman Filtering

P. Henkel, P. Jurkowski, and C. Gunther

Abstract:	In this paper, a new maximum a posteriori probability estimation of ambiguities and baselines is proposed for differential carrier phase positioning. It performs a recursive least-squares estimation with an extended Kalman filter, that uses double difference code and carrier phase measurements and Gaussian a priori knowledge about the baseline length, elevation/ pitch angle and azimuth/ heading. The maximum a posteriori probability estimator finds the optimum trade-off between a solution that minimizes the range residuals and one which is close to the priori knowledge. It is shown that the Gaussian a priori knowledge enables a ten times faster convergence of the float solution, and it substantially suppresses multipath and, thereby, prevents divergence of float ambiguities and baselines. Moreover, the Gaussian a priori knowledge allows some errors in the a priori information, i.e. it is more robust than deterministic a priori knowledge.
Published in:	Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011) September 20 - 23, 2011 Oregon Convention Center, Portland, Oregon Portland, OR
Pages:	3881 - 3888
Cite this article:	Henkel, P., Jurkowski, P., Gunther, C., "Differential Integer Ambiguity Resolution with Gaussian a Priori Knowledge and Kalman Filtering," Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011), Portland, OR, September 2011, pp. 3881-3888.
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