A new method for the calibration of regional ionospheric delay based on uncombined precise point positioning (U-PPP) is proposed in this study. The performance of the new method was comparatively validated in terms of its accuracy and robustness with respect to the phase-smoothed pseudorange (PSP) method through two short-baseline experiments. Accuracy of the PPP-derived ionospheric delays was further assessed by interpolating them to a user station to perform single-frequency simulated kinematic PPP. Two 24-hr period datasets of four continuous operation reference system (CORS) stations were analyzed, collected during calm and disturbed ionospheric conditions, respectively. The single-frequency GPS observables from a user station, that were a-priori corrected by the interpolated ionospheric delays, were utilized to implement single-frequency PPP (SF-PPP). The results show that interpolation accuracy is better than 1 dm and, with the proposed method, is less affected by the ionospheric activity; meanwhile, positioning accuracy of SF-PPP was 4∼5 cm (horizontal) and better than 1 dm (vertical). For comparison, two reference SF-PPP solutions were also obtained, in which the ionospheric delays are eliminated either by forming semi-combination observations or by using global ionosphere maps (GIM) model values; in both cases the positioning accuracy was only 4∼7 dm (horizontal) and 1 m (vertical). These results provide a further demonstration of the performance of PPP-based regional ionospheric calibration in the parameter domain.

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With the development of various navigation systems (such as GLONASS, Galileo, BDS), there is a sharp increase in the number of visible satellites. Accordingly, the probability of multiply gross measurements will increase. However, the conventional RAIM methods are difficult to meet the demands of the navigation system. In order to solve the problem of checking and identify multiple gross errors of receiver autonomous integrity monitoring (RAIM), this paper designed full matrix of single point positioning by QR decomposition, and proposed a new RAIM algorithm based on fuzzy clustering analysis with fuzzy c-means (FCM). And on the condition of single or two gross errors, the performance of hard or fuzzy clustering analysis were compared. As the results of the experiments, the fuzzy clustering method based on FCM principle could detect multiple gross error effectively, also achieved the quality control of single point positioning and ensured better reliability results.

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