Real-time GNSS precise point positioning with smartphones for vehicle navigation

Li, Zishen; Wang, Liang; Wang, Ningbo; Li, Ran; Liu, Ang

doi:10.1186/s43020-022-00079-x

Cited by 23 publications

(8 citation statements)

References 31 publications

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“…Aggrey et al [ 19 ] compared PPP performance for four smartphones under static and kinematic experiments, and the MI 8 achieved 40 cm rms in the horizontal direction, which was far better than other single-frequency smart devices. In addition, similar performance can be also seen from numerous recent contributions with real-time and final products [ 20 , 21 , 22 , 23 , 24 ]. Continuing this research, recent studies prove that dual-frequency smartphones can provide lane-level navigation processed with both RTK and PPP technologies in realistic driving environments [ 25 , 26 ], and the solutions can be further improved with the aid of smartphone native Inertial Measurement Unites (IMUs) [ 27 , 28 ].…”

Section: Introductionsupporting

confidence: 64%

A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments

Bisnath

2023

Sensors

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Precise positioning using smartphones has been a topic of interest especially after Google decided to provide raw GNSS measurement through their Android platform. Currently, the greatest limitations in precise positioning with smartphone Global Navigation Satellite System (GNSS) sensors are the quality and availability of satellite-to-smartphone ranging measurements. Many papers have assessed the quality of GNSS pseudorange and carrier-phase measurements in various environments. In addition, there is growing research in the inclusion of a priori information to model signal blockage, multipath, etc. In this contribution, numerical estimation of actual range errors in smartphone GNSS precise positioning in realistic environments is performed using a geodetic receiver as a reference. The range errors are analyzed under various environments and by placing smartphones on car dashboards and roofs. The distribution of range errors and their correlation to prefit residuals is studied in detail. In addition, a comparison of range errors between different constellations is provided, aiming to provide insight into the quantitative understanding of measurement behavior. This information can be used to further improve measurement quality control, and optimize stochastic modeling and position estimation processes.

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Section: Introductionsupporting

confidence: 64%

A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments

Bisnath

2023

Sensors

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“…The pseudorange observation is the travelling time of the signal to propagate from the satellite to the receiver (here smartphone). It is of the form [ 24 ]:

where

is the pseudorange observation in meter,

is the received time (measurement time) in nanosecond,

is the received GNSS satellite time at the measurement time in nanosecond reported in the CSV file (one of the variables in the GNSSMeasurement class) and

is the speed of light. The measurement time

in GNSS time system in nanosecond is as follows:

where

is the GNSS receiver’s internal hardware clock value,

is the time offset at which the measurement was taken,

is the difference between

inside the GPS receiver and the true GPS time since 6 January 1980 and

is the clock’s sub-nanosecond bias.…”

Section: Access To Android Raw Gnss Measurements and Gnss Observation...mentioning

confidence: 99%

“…Based on the results, the mixed frequency model could effectively improve the positioning performance compared to the traditional dual-frequency PPP and the single-frequency PPP. Li et al [ 24 ] proposed a real-time PPP algorithm for land vehicle navigation with smart devices. The smartphones were placed on the roof and the dashboard.…”

Section: Introductionmentioning

confidence: 99%

GNSS Observation Generation from Smartphone Android Location API: Performance of Existing Apps, Issues and Improvement

Zangeneh-Nejad

Yang

Gao

2023

Sensors

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Precise position information available from smartphones can play an important role in developing new location-based service (LBS) applications. Starting from 2016, and after the release of Nougat version (Version 7) by Google, developers have had access to the GNSS raw measurements through the new application programming interface (API), namely android.location (API level 24). However, the new API does not provide the typical GNSS observations directly (e.g., pseudorange, carrier-phase and Doppler observations) which have to be generated by the users themselves. Although several Apps have been developed for the GNSS observations generation, various data analyses indicate quality concerns, from biases to observation inconsistency in the generated GNSS observations output from those Apps. The quality concerns would subsequently affect GNSS data processing such as cycle slip detection, code smoothing and ultimately positioning performance. In this study, we first investigate algorithms for GNSS observations generation from the android.location API output. We then evaluate the performances of two widely used Apps (Geo++RINEX logger and GnssLogger Apps), as well as our newly developed one (namely UofC CSV2RINEX tool) which converts the CSV file to a Receiver INdependent Exchange (RINEX) file. Positioning performance analysis is also provided which indicates improved positioning accuracy using our newly developed tool. Future work finding out the potential reasons for the identified misbehavior in the generated GNSS observations is recommended; it will require a joint effort with the App developers.

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“…The fact that this study is based on u-blox instruments operating in the L1/E1 and L2 frequency bands, smartphones operating in the L1/E1 and L5/E5a frequency bands, as well as a geodetic instrument receiving observations at all frequencies, was a good starting point for further analysis. A valuable study on real-time kinematic positioning with smartphones was recently published by Li et al [42]. The study used two Huawei Mate30 and two Huawei P40 smartphones with two installation modes: vehicle roof mode, in which the smartphones were mounted on the roof outside the vehicle, and dashboard mode, in which the smartphones were stabilised inside the vehicle.…”

Section: Introductionmentioning

confidence: 99%

The Efficiency of Geodetic and Low-Cost GNSS Devices in Urban Kinematic Terrestrial Positioning in Terms of the Trajectory Generated by MMS

et al. 2023

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The quality of geospatial data collection depends, among other things, on the reliability and efficiency of the GNSS receivers or even better integrated GNSS/INS systems used for positioning. High-precision positioning is currently not only the domain of professional receivers but can also be achieved by using simple devices, including smartphones. This research focused on the quality of 2D and 3D kinematic positioning of different geodetic and low-cost GNSS devices, using the professional mobile mapping system (MMS) as a reference. Kinematic positioning was performed simultaneously with a geodetic Septentrio AsteRx-U receiver, two u-blox receivers—ZED-F9P and ZED-F9R—and a Xiaomi Mi 8 smartphone and then compared with an Applanix Corporation GPS/INS MMS reference trajectory. The field tests were conducted in urban and non-urban environments with and without obstacles, on road sections with large manoeuvres and curves, and under overpasses and tunnels. Some general conclusions can be drawn from the analysis of the different scenarios. As expected, some results in GNSS positioning are subject to position losses, large outliers and multipath effects; however, after removing them, they are quite promising, even for the Xiaomi Mi8 smartphone. From the comparison of the GPS and GNSS solutions, as expected, GNSS processing achieved many more solutions for position determination and allowed a relevant higher number of fixed ambiguities, even if this was not true in general for the Septentrio AsteRx-U, in particular in a surveyed non-urban area with curves and serpentines characterised by a reduced signal acquisition. In GNSS mode, the Xiaomi Mi8 smartphone performed well in situations with a threshold of less than 1 m, with the percentages varying from 50% for the urban areas to 80% for the non-urban areas, which offers potential in view of future improvements for applications in terrestrial navigation.

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Real-time GNSS precise point positioning with smartphones for vehicle navigation

Cited by 23 publications

References 31 publications

A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments

A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments

GNSS Observation Generation from Smartphone Android Location API: Performance of Existing Apps, Issues and Improvement

The Efficiency of Geodetic and Low-Cost GNSS Devices in Urban Kinematic Terrestrial Positioning in Terms of the Trajectory Generated by MMS

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