Enhanced network RTK positioning method accounting for ionospheric residual interpolation uncertainty in low-latitude regions

Abstract High-precision regional ionospheric interpolation is essential for achieving reliable positioning in network RTK. However, in challenging scenarios such as long baselines and highly active ionospheric conditions at low latitudes, ionospheric delays are difficult to accurately resolve through regional interpolation. As a result, the residual ionospheric interpolation error exhibits considerable uncertainty, which severely degrades the service performance of network RTK. Existing studies have highlighted the necessity of jointly optimizing both the server-side and user-side algorithms. In response, this paper proposes an enhanced network RTK approach—the ionospheric stochastic constraint method (ISC)—which integrates a server-side ionospheric residual interpolation uncertainty indicator with a user-side ionospheric weighting model. Experiments were conducted using data from a long-baseline reference station network in a low-latitude region, with an average baseline length of 104 km. Using the virtual reference station (VRS) method as a benchmark, the performance of the ISC-based network RTK algorithm was thoroughly evaluated. The results demonstrate that, compared to the VRS method, the mean value of the 90th percentile horizontal error under the ISC method was reduced from 5.6 cm to 1.9 cm, and the accuracy was improved by 66.1%. The mean value of the 90th percentile of vertical error decreased from 10.2 cm to 4.4 cm, with an improvement of 56.7%. The average daily fixed rate of ambiguity increased from 80.4% to 97.6%. In addition, the ISC method achieves a good balance between positioning accuracy and convergence speed, and can basically complete the initialization within 10 epochs, which can meet the timeliness requirements of real-time positioning.