An optimized stochastic model for smartphone GNSS positioning

Abstract

With the increasing popularity of high-precision applications of smartphone, more and more scholars carry out studies in the field of smartphone GNSS positioning. In the previous studies, more attention has been paid to data quality control, data preprocessing and observation models. However, the research on stochastic models is rare. The stochastic model is significant for the subsequent optimal positioning parameter estimation, meanwhile, the stochastic models of smartphones and professional geodetic receivers are very different for the completely different characteristics of smartphone GNSS observations. It is necessary to develop a stochastic model suitable for smartphone observations. Based on the characteristics of smartphone observations, this paper proposes an optimized stochastic model. The specific process of this method is as follows: firstly, the code-minus-phase (C-L) combi-nation and double-differenced measurement were used to quantify the noise of smartphone code and phase observations. Then, an optimized carrier-to-noise density ratio (C/N0) dependent stochastic model was proposed on the basis of the characteristic of smartphone observations. To validate the superiority of the proposed model, single point positioning (SPP) and real time kinematic (RTK) experiments were carried out by Xiaomi 8 in 2 days. The 3-dimensional root mean squares (RMS) of SPP were 6.18 and 5.38 m, with improvements varying within 0.00%-10.02% compared with the customary models. Likewise, the RMS of RTK were 0.14 and 0.24 m, with improvements fitting in range of 10.06%-39.92%. This research plays an important role in improving the positioning accuracy of smartphone and promoting the popularization of high-precision applications.

A Stochastic Model Based on Optimal Satellite Subset Selection Strategy for Smartphone Pseudorange Relative Positioning

“…The C/N0, which gauges signal reception quality, has been shown to be more optimized for smartphone positioning than the elevation-dependent model [ 8 , 24 ]. Recognizing the limitations of C/N0 in smartphone positioning, recent advancements include the elevation-C/N0 model, which merges both indicators (elevation and C/N0) with robust estimation principles [ 25 ], and the optimized C/N0-dependent stochastic model, which aptly describes smartphone GNSS observation quality [ 29 ]. These models have, to a degree, ameliorated smartphone positioning.…”

Determining the Antenna Phase Center for the High-Precision Positioning of Smartphones

“…Consequently, this groundbreaking smartphone has been extensively studied by scholars worldwide in the field of smartphone positioning. For instance, Sui Mingming et al [5] introduced an optimized random model tailored to the characteristics of smartphone observations. Through this approach, they successfully enhanced the positioning accuracy of smartphones, contributing to the growing body of research on leveraging the capabilities of dual-frequency GNSS smartphones like the Xiaomi Mi8 smartphone.…”

Improving the stochastic model for code pseudorange observations from Android smartphones

“…These studies are mainly limited to the most common models used for comparing the C/ N0-dependent and elevation-dependent weighting schemes, not proposing any special approach for smartphone observations. Sui et al (2022) have proposed a stochastic model optimized for smartphone observations and showed that the positioning performance of SPP (single point positioning) and RTK methods can be improved between 10% and 40%, considering three-dimensional RMS errors. Zangenehnejad and Gao (2023) have presented the C/N0/elevation-based model for smartphone observations depending on the LS-VCE (least-square variance component estimation) method and showed an improvement of 25.1% in PPP solution, considering horizontal RMS errors.…”