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Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The existing cycle-slip detection algorithm usually can not detect and repair cycle-slips correctly in the ionospheric activity phase or when magnetic storms happen, this paper proposes a new algorithm for the fitted residual of the ionospheric total electronic content(TEC) rate (TECR) based on data quality analysis. By analyzing the data quality of the fitted TECR residual, we determine the optimal fitted epoch count adaptively and with this fitted epochs count can obtain a fitted TECR to weaken the influence of ionospheric error effectively. In order to ensure that the repaired cycle-slip is correct, the repaired value is validated by search re-determination and TECR complementary detection. Measured data is used to validated this algorithm. The experimental results show that this algorithm can realize the zero-difference dynamic real-time cycle-slip detection and repair the ionospheric activity phase. [ABSTRACT FROM AUTHOR]

In order to improve the prediction precision of navigation satellite clock bias in the medium and long term, we design a new prediction model using a wavelet neural network based on a new data preprocessing method, aimed at processing the single difference sequence of satellite clock bias data. Specifically, this model firstly makes difference between two values of adjacent epoch for the given clock bias data, thus obtaining the corresponding single difference sequence, and then uses the proposed preprocessing method to process the sequence, and adopts the preprocessed sequence when modeling a wavelet neural network to predict the following medium- and long-term sequences. Finally,the proposed model restores the predicted sequences to the corresponding prediction clock bias. Using clock bias data from satellite-bone rubidium clocks in GPS, we conducted medium- and long-term prediction tests for the new method, simultaneously comparing it with three common prediction methods ;the quadratic polynomial model, grey model, and Kalman filter model. The results show that the new method can effectively reduce the prediction error in the medium- and long-term satellite clock bias prediction. [ABSTRACT FROM AUTHOR]

Copyright of Hydrographic Surveying & Charting / Haiyang Cehui is the property of Hydrographic Surveying & Charting Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)