Abstract
A new approach, Bayesian method for single frequent cycle slips detection based on autoregressive model, is presented by exploiting modern Bayesian statistical theory. Besides, this paper deals with the problem of masking and swamping about cycle slips detection in a thorough new conception and gives the corresponding solution. First of all, considering the character of cycle slips in phase data and the relations between cycle slips and outliers, this paper proposes a Bayesian method for cycle slips detection based on the posterior probabilities of classification variables in the respective of Bayesian hypothesis. Secondly, an adaptive Gibbs sampling algorithm is designed through analyzing the reasons of masking and swamping about cycle slips detection. Then an unmasking Bayesian method for cycle slips detection is proposed. Thirdly, accurate estimation of cycle slips is given based on Bayesian point estimation. Finally, the method is applied to real phase data to test its correction and efficiency.
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Acknowledgments
This research was supported jointly by National Science Foundation of China (No. 40974009, No. 41174005), Planned Research Project of Technology of Zhengzhou City, and Funded Project with youth of Annual Meeting of China’s satellite navigation.
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Zhang, Q., Gui, Q., Li, J., Gong, Y., Han, S. (2012). Bayesian Methods for Cycle Slips Detection Based on Autoregressive Model. In: Sun, J., Liu, J., Yang, Y., Fan, S. (eds) China Satellite Navigation Conference (CSNC) 2012 Proceedings. Lecture Notes in Electrical Engineering, vol 160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29175-3_29
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DOI: https://doi.org/10.1007/978-3-642-29175-3_29
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