Smartphone positioning methods relying solely on global navigation satellite system (GNSS) are susceptible to significant errors in urban environments due to challenging observation conditions. To enhance position accuracy, this article introduces a novel fifth-generation (5G) observation model based on unit vectors and evaluates its performance using several user locations and precision observation data. The results demonstrate that the unit vector method, compared to the traditional angle of departure (AOD) approach, effectively reduces the nonlinearity of the observation model, leading to a substantial improvement in the 5G system’s positioning accuracy. Furthermore, we conduct experiments to investigate the impact of the geometric distribution of 5G base station (BS) height on positioning accuracy. The results reveal a significant influence, highlighting the importance of considering BS height in the positioning process. In the urban environment, introducing 5G BS angle observations as an aid to GNSS significantly enhances the reliability of smartphone positioning, achieving positioning errors of less than one meter. Notably, this approach outperforms the introduction of 5G distance observations, providing valuable insights for the development of seamless indoor and outdoor positioning systems in the future.

As one of the main errors that affects Global Navigation Satellite System (GNSS) positioning accuracy, ionospheric delay also affects the improvement of smartphone positioning accuracy. The current ionospheric error correction model used in smartphones has a certain time delay and low accuracy, which is difficult to meet the needs of real-time positioning of smartphones. This article proposes a method to use the real-time regional ionospheric model retrieved from the regional Continuously Operating Reference Stations (CORS) observation data to correct the GNSS positioning error of the smartphone. To verify the accuracy of the model, using the posterior grid as the standard, the electron content error of the regional ionospheric model is less than 5 Total Electron Content Unit (TECU), which is about 50% higher than the Klobuchar model, and to further evaluate the impact of the regional ionosphere model on the real-time positioning accuracy of smartphones, carrier-smoothing pseudorange and single-frequency Precise Point Positioning (PPP) tests were carried out. The results show that the real-time regional ionospheric model can significantly improve the positioning accuracy of smartphones, especially in the elevation direction. Compared with the Klobuchar model, the improvement effect is more than 34%, and the real-time regional ionospheric model also shortens the convergence time of the elevation direction to 1 min. (The convergence condition is that the range of continuous 20 s is less than 0.5 m).

When calibrating inter-system biases (ISB), especially the fractional part of inter-system phase biases (F-ISPB), a multi-GNSS inter-system model can effectively improve positioning performance under a complex environment. Usually, the F-ISPB is estimated after fixing the intra-system ambiguities. However, this approach seems inapplicable when it is difficult to obtain intra-system ambiguities under a complex environment. A multi-dimensional particle filter (PF)-based F-ISPB estimate method has been proposed to overcome the problem. Nevertheless, the multi-dimensional PF involves a great quantity of computations. In this contribution, four state optimal estimate-based F-ISPB handling schemes are proposed: step-by-step PF, step-by-step particle swarm optimization (PSO), multi-dimensional PF, and multi-dimensional PSO-based F-ISPB estimate methods. Two baselines were selected to investigate the F-ISPB estimate performance in both open and complex environments. The results show that due to the potential of the wrong F-ISPB to bring about the maximum ratio for a long time during the initial stage, the step-by-step PF method can achieve better performance than step-by-step PSO. Besides, the two-dimensional results show that all of the F-ISPB still cannot be extracted under complex environments by multi-dimensional PSO. Furthermore, compared with step-by-step PF, the multi-dimensional PF method costs too much to obtain the right value. For example, in the two-dimensional case, the step-by-step PF searches 200 times for each epoch, while the two-dimensional PF requires 40 000 times for each epoch, so it is difficult for receivers to provide hardware support for this method. In addition, the step-by-step PF can obtain the right F-ISPB with about 100 epochs no matter what scenario. Thus, under challenging observation scenarios, a step-by-step PF method is recommended to extract the F-ISPB.

By calibrating the inter-system bias (ISB), an inter-system model can be achieved to improve the performance of multi-global navigation satellite system (GNSS) positioning. A number of studies were carried out to compose inter-system models among code division multiple access (CDMA) systems. However, due to the frequency division multiple access (FDMA) technique, GLONASS phase and code observations suffer from inter-frequency phase bias (IFPB) and inter-frequency code bias (IFCB) respectively, which make it difficult to obtain the ISB among GLONASS and CDMA systems. In addition, most of the current inter-system models are unable to handle mixed frequencies. A new single difference-based uniform inter-system model suitable for all of the CDMA and FDMA frequency combinations is proposed in this study. We first utilize a particle filter approach to extract the IFPB rate. Afterwards, the multi-ISB parameters can be estimated by the proposed model simultaneously. Four short baselines containing the signals of GLONASS, GPS, BDS3, BDS2, Galileo, and QZSS are selected to validate the model. The quantitative results indicate that the IFPB rate can be extracted precisely by a particle filter within ten epochs. The inter-system phase bias valuations are sufficiently stable on a daily scale and have standard deviation near 0.01 cycles. After de-weighting the GLONASS code observations to reduce the influence of IFCB, the inter-system code bias is also stable. Furthermore, imposing the stable ISB on the multi-GNSS inter-system model can effectively improve the single-frequency real-time kinematic positioning accuracy and reliability for severely obstructed situations with only a small number of satellites observed.