On January 5, 2025, the PRIDE team released PRIDE PPP-AR v3.1, which includes multipath modeling and correction. Its main feature is the ability to perform joint multipath modeling for overlap-frequency signals (i.e. GPS L1/L5, Galileo E1/E5a, and BDS B1C/B2a), serving high-precision positioning and identification and extraction of geological signals. Welcome everyone to test and provide feedback and suggestions. Please refer to the references at the end of the tweet.

1.Module Introduction:

    The MHM module is a newly added module for multipath modeling in PRIDE PPP-AR v3.1. It can model multiple days of data to compensate for the multipath delay of the station. This module is based on the multipath hemispherical map (MHM) and supports multipath modeling for any dual frequency of all constellation. Although the improvement in positioning precision after multipath compensation is related to the environment, data quality, and integrity of the station, on average, multipath compensation improves the precision of floating solutions by about 5-20% and fixed solutions by about 5-10%.

    PRIDE PPP-AR software runs according the structure shown in the following figure, the process procedures are divided into three modules, least-squares estimator and integer ambiguity resolution in addition to a data preparation and pre-processing module. The first part, data preparation and pre-processing, prepare table file and precise products for following data process. The spp (standard point positioning) module will be used in this part to calculate the prior positions of station. The function of sp3orb (SP3 orbit) is to transform SP3 orbit into a self-defined binary format. Then, the software can efficiently access the precise orbit products. If multipath correction is performed, mhm will generate the corresponding "mhm-file” to provide multipath correction values in the least-squares section. In least-squares estimator part, tedit is used to make data tentative pre-processing and generate “log-file” to record the RINEX (The Receiver Independent Exchange Format) health diagnosis information. Once got the “log-file”, lsq (least-square adjustment) module can realize parameter estimation and output results. Then used redig (a posteriori residual diagnosis) module, the residuals can be processed and new “log-file” can be generated. By the iteration of lsq and redig, data cleaning is completed. If ambiguity is not fixed, the ambiguity-float solution can be obtained. Otherwise, the module named arsig (ambiguity resolution at a single receiver) will be used to realize wide-lane and narrow-lane ambiguities resolution. In the next round lsq processing, these integer ambiguities will be introduced as hard constraints to achieve ambiguity-fixed solutions.

    mhm is used to construct the MHM model. Firstly, determine whether the “res-file” in the mhm folder was generated through F/S mode and whether it belongs to the same station. Secondly, divide the residuals of each satellite in each “res-file” into a 1 °× 1 ° grid according to their corresponding elevation and azimuth angles, and construct a 90 × 360 grid. Finally, perform quality control on the residuals in each 1 °× 1 ° grid, remove coarse errors, and output the mean value after removal as the multipath correction value for that grid to the “mhm-file”.

2.Module features: 

    In addition to traditional MHM models, the module also supports establishing interoperable MHM models using overlap-frequency signals, reducing the number of observation duration required for modeling while increasing the generality of the model. The algorithm details can be found at: https://satellite-navigation.springeropen.com/articles/10.1186/s43020-024-00144-7

3.References:

Geng J, Zhang H, Li G, Aoki Y (2024) Multipath mitigation for GPS/Galileo/BDS-3 precise point positioning with overlap-frequency signals. Satellite Navigation. 5, 22. doi:10.1186/s43020-024-00144-7

Geng J, Li P, Li G (2024) Combining the GPS/Galileo/BDS-3 signals on overlap frequencies for interoperable multipath hemispherical maps. J Geod. doi:10.1007/s00190-024-01841-6