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Shinghal and Bisnath Satell Navig (2021) 2:10 Satellite Navigation
https://doi.org/10.1186/s43020-021-00042-2
https://satellite-navigation.springeropen.com/

ORIGINAL ARTICLE Open Access

Conditioning and PPP processing

of smartphone GNSS measurements in realistic

environments

*
Ganga Shinghal and Sunil Bisnath

Abstract
Smartphones typically compute position using duty-cycled Global Navigation Satellite System (GNSS) L1 code
measurements and Single Point Positioning (SPP) processing with the aid of cellular and other measurements. This
internal positioning solution has an accuracy of several tens to hundreds of meters in realistic environments (hand-
held, vehicle dashboard, suburban, urban forested, etc.). With the advent of multi-constellation, dual-frequency GNSS
chips in smartphones, along with the ability to extract raw code and carrier-phase measurements, it is possible to use
Precise Point Positioning (PPP) to improve positioning without any additional equipment. This research analyses GNSS
measurement quality parameters from a Xiaomi MI 8 dual-frequency smartphone in varied, realistic environments.
In such environments, the system sufers from frequent phase loss-of-lock leading to data gaps. The smartphone
measurements have low and irregular carrier-to-noise (C/N ) density ratio and high multipath, which leads to poor or
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no positioning solution. These problems are addressed by implementing a prediction technique for data gaps and a
C/N -based stochastic model for assigning realistic a priori weights to the observables in the PPP processing engine.
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Using these conditioning techniques, there is a 64% decrease in the horizontal positioning Root Mean Square (RMS)
error and 100% positioning solution availability in sub-urban environments tested. The horizontal and 3D RMS were
20 cm and 30 cm respectively in a static open-sky environment and the horizontal RMS for the realistic kinematic sce-
nario was 7 m with the phone on the dashboard of the car, using the SwiftNav Piksi Real-Time Kinematic (RTK) solu-
tion as reference. The PPP solution, computed using the YorkU PPP engine, also had a 5–10% percentage point more
availability than the RTK solution, computed using RTKLIB software, since missing measurements in the logged fle
cause epoch rejection and a non-continuous solution, a problem which is solved by prediction for the PPP solution.
The internal unaided positioning solution of the phone obtained from the logged NMEA (The National Marine Elec-
tronics Association) fle was computed using point positioning with the aid of measurements from internal sensors.
The PPP solution was 80% more accurate than the internal solution which had periodic drifts due to non-continuous
computation of solution.
Keywords: PPP, Smartphone, Realistic environment, Prediction, C/N -based stochastic modeling, Internal phone
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solution, Positioning solution comparison

Introduction vehicle navigation and is now being expanded to aug-
Global Navigation Satellite System (GNSS) based posi- mented reality-based gaming, tourism applications,
tioning in smartphones has been used for personal and contact tracing, bicycle rentals, etc. Most cellphones
and smartphones generally had extremely low-cost
Global Positioning System (GPS) single-frequency
*Correspondence: ganga22@yorku.ca chips tracking GPS L1 C/A-code measurements with
Department of Earth and Space Science and Engineering, Lassonde low-cost antennas. Smartphone GNSS chipsets output
School of Engineering, York University, Toronto, Canada

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