Multifrequency Signal Characterization and Assessment of GNSS Positioning in Smartphone

The need for high-precision and dependable navigation and positioning services is rising in the mass market as a result of the BDS-3’s completion and the performance development of smartphone chips. However, compared with the geodesic receiver, the positioning accuracy of smartphones is severely degraded due to the low antenna gain and poor anti-interference ability. In March 2022, Redmi released K50 smartphones that support BDS tri-frequency, providing conditions for further improvement of smartphone positioning. First, we collected the multi-constellation and multi-frequency data of the K50 to evaluate the availability of tri-frequency observations. Then, we compared the observations between different frequencies and found that the pseudorange data quality of the L5/E5a is better than that of L1/E1. For the BDS, the pseudorange data quality of the B2a is obviously better than that of the other two frequencies. For carrier observations, expect for the initial phase biases of B1C, there is no significant difference in data quality between different frequencies. It means that the traditional random model cannot accurately reflect the pseudorange quality of smartphone observation. We used the zero-baseline double difference residual method to calculate the smartphone observation accuracy and refined the random model. Finally, we verified the performance of the refined model through location tests. The results show that the model refinement can significantly improve positioning accuracy and reliability in the single point positioning test and shorten the convergence time in relative positioning. In kinematic positioning, the improved model can obtain the positioning accuracy of a single epoch centimeter-level even when the signal is slightly blocked, which is of great significance for high-precision navigation of smartphones in urban environments.