Statistical analysis and quality control for GPS fractional cycle bias and integer recovery clock estimation with raw and combined observation models

With the development of Ambiguity Resolution for Precise Point Positioning (PPP-AR) over the past years, there are three representative PPP-AR methods up to now: Fractional Cycle Bias (FCB) method, Integer Recovery Clock (IRC) method and Decoupled Satellite Clock (DSC) method. In all these methods, satellite products are generated from a global or regional reference network and then broadcast to users for ambiguity fixing. Positioning accuracy with short-time observations can be significantly improved after ambiguities are correctly fixed; nevertheless, high-accuracy satellite products are the prerequisite of reliable ambiguity resolution. Therefore, quality control for PPP-AR at network end is of great significance. In this paper, FCB and IRC estimation methods based on Ionosphere-Free PPP (IF-PPP) and Uncombined PPP (U-PPP) are investigated. In addition, quality control strategy for FCB and IRC estimation is proposed. Data sets from International GNSS Service (IGS) network and CORSnet-NSW in Australia are used to validate and demonstrate the performance of the proposed method. The results indicate that reliable PPP-AR can be achieved with the estimated FCB and IRC products, similar PPP-AR performance is revealed with IF-PPP and U-PPP. After ambiguities are successfully fixed, positioning accuracy in the east, north and up directions can reach 0.7, 0.9 and 3.1 cm, respectively, which is a considerable improvement as compared with ambiguity-float PPP solution. Moreover, the FCB method slightly outperforms the IRC method due to the better reliability of FCB estimation. In addition, accurate ionospheric delay can be derived from a regional reference network to enhance the model strength of U PPP, thus the Time-To-First-Fix (TTFF) can be further shortened. Even instantaneous ambiguity fixing is achievable and the positioning accuracy can reach 0.1, 1.2 and 0.4 cm. (C) 2017 COSPAR. Published by Elsevier Ltd. All rights reserved.