Abstract

Achieving continuous and high-precision positioning services via smartphone under a Global Navigation Satellite System (GNSS)-degraded environment is urgently demanded by the mass market. In 2018, Xiaomi launched the world’s first dual-frequency GNSS smartphone, Xiaomi Mi 8. The newly added L5/E5 signals are more precise and less prone to distortions from multipath reflections. This paper discusses the characteristics of raw dual-frequency GNSS observations from Xiaomi Mi 8 in urban environments; they are characterized by high pseudorange noise and frequent signal interruption. The traditional dual-frequency ionosphere-free combination is not suitable for Xiaomi Mi 8 raw GNSS data processing, since the noise of the combined measurements is much larger than the influence of the ionospheric delay. Therefore, in order to reasonably utilize the high precision carrier phase observations, a time differenced positioning filter is presented in this paper to deliver continuous and smooth navigation results in urban environments. The filter first estimates the inter-epoch position variation (IEPV) with time differenced uncombined L1/E1 and L5/E5 carrier phase observations and constructs the state equation with IEPV to accurately describe the user’s movement. Secondly, the observation equations are formed with uncombined L1/E1 and L5/E5 pseudorange observations. Then, kinematic experiments in open-sky and GNSS-degraded environments are carried out, and the proposed filter is assessed in terms of the positioning accuracy and solution availability. The result in an open-sky environment shows that, assisted with L5/E5 observations, the root mean square (RMS) of the stand-alone horizontal and vertical positioning errors are about 1.22 m and 1.94 m, respectively, with a 97.8% navigation availability. Encouragingly, even in a GNSS-degraded environment, smooth navigation services with accuracies of 1.61 m and 2.16 m in the horizontal and vertical directions are obtained by using multi-GNSS and L5/E5 observations.

Highlights

  • According to the Global Navigation Satellite System (GNSS) Market 2017 Report, about 6.4 billion smartphones embedded with GNSS chipsets will be active worldwide by 2020 [1]

  • We focus on the standalone smartphone navigation problem in GNSS-degraded environments

  • According to the aforementioned characteristics analysis of the pseudorange and carrier phase observations of Xiaomi Mi 8 in Section 3, it can be summarized that, in static environments, the pseudorange outlier percentages and carrier phase cycle slips percentages are low, while they increase obviously in dynamic environments; in dynamic GNSS-degraded environments, the phase cycle slip percentages are higher than 30.31%, even at 62.25% for GLObal NAvigation Satellite System (GLONASS) satellites

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Summary

Introduction

According to the Global Navigation Satellite System (GNSS) Market 2017 Report, about 6.4 billion smartphones embedded with GNSS chipsets will be active worldwide by 2020 [1]. Conventional dual-frequency (ionosphere-free combination of pseudorange and carrier phase) and single-frequency precise point positioning (PPP) with raw GPS + Galileo observations from Xiaomi Mi 8 showed that, in the static open environment, decimeter-level positioning accuracy was obtained after about 102 min. The characteristic of raw multi-GNSS observations from Xiaomi Mi 8 under static open and dynamic complex environments is assessed in terms of C/N0, noise of pseudorange, and carrier phase observations, and the approximate percentage of pseudorange gross errors and carrier phase cycle slips. Considering the frequent signal interruptions and carrier phase cycle slips of raw Xiaomi Mi 8 observations under a complex navigation environment, an improved time-differenced navigation algorithm using raw dual-frequency multi-GNSS measurements from the smartphone is proposed.

Data Collection
Static GNSS Data Collection
Pseudorange Observations
Time Differenced Filter Algorithm
State Equation
Observation Equation
Filter Model
Positioning Strategies
Static Test Analysis
Findings
Dynamic Test Analysis
Full Text
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