Python']Python toolbox for BDS PPP-B2b and Galileo HAS decoding and its products performance validation

Precision Point Positioning (PPP) technology can achieve decimeter to centimeter level positioning accuracy, which is essential for applications in industries such as autonomous driving, modern agriculture, and drone logistics. The PPP-B2b service provided by BDS and the High Accuracy Service (HAS) offered by Galileo, capable of achieving decimeter-level accuracy without relying on ground communication networks or commercial augmentation corrections, presents substantial potential for various industries. However, challenges persist when users try to utilize and evaluate these corrections, such as the lack of open-source software capable of decoding these products and the absence of product archives necessary for such evaluations. To address these challenges, we present a comprehensive Python toolbox called NavDecoder, which can decode both PPP-B2b and Galileo HAS products and provide archived data in ASCII format, facilitating its application. We also evaluated the accuracy of the two products, using the root mean square (RMS) of signal-in-space range error as a metric, with final products from Wuhan University serving as a benchmark. The average RMS was found to be 1.0 m for GPS in PPP-B2b and 0.62 m for GPS in Galileo HAS. For BDS in PPP-B2b, the average RMS was 0.67 m, while for Galileo in HAS, it was 0.18 m. Then, the accuracy of PPP-B2b and Galileo HAS products were validated using both static and kinematic PPP. A forward Kalman filter was employed for static PPP, while a combined forward and backward filter was used for kinematic PPP. The results demonstrate that comparable accuracy can be achieved through two positioning modes. BDS PPP-B2b offers superior positioning accuracy compared to Galileo HAS. Both products achieve an accuracy of 0.1 m with hourly PPP, with 4-hour observation periods yielding the most significant accuracy improvements.