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GNSS Observation Generation from Smartphone Android Location API: Performance of Existing Apps, Issues and
Farzaneh Zangenehnejad1, Yang Jiang1, Yang Gao1
1Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.
Sensors (Basel, Switzerland)
|January 21, 2023
Summary
New tools improve Global Navigation Satellite System (GNSS) observation generation from smartphones. Our UofC CSV2RINEX tool enhances positioning accuracy compared to existing apps, addressing data quality concerns.
Area of Science:
- Geomatics Engineering
- Satellite Navigation Systems
- Mobile Sensing
Background:
- Smartphones offer precise positioning crucial for location-based services (LBS).
- Android's `android.location` API (level 24+) provides Global Navigation Satellite System (GNSS) raw measurements.
- Existing methods require manual generation of GNSS observations (pseudorange, carrier-phase, Doppler) from API data, with reported quality issues.
Purpose of the Study:
- To investigate algorithms for generating GNSS observations from smartphone API data.
- To evaluate the performance of existing GNSS data logging applications.
- To develop and assess a new tool for improved GNSS observation generation and positioning accuracy.
Main Methods:
- Algorithm investigation for GNSS observation generation from `android.location` API.
- Performance evaluation of Geo++RINEX logger and GnssLogger applications.
- Development and testing of the UofC CSV2RINEX tool for converting CSV to Receiver INdependent Exchange (RINEX) files.
- Analysis of positioning performance using generated GNSS data.
Main Results:
- Identified quality concerns (biases, inconsistency) in GNSS observations generated by existing apps.
- The developed UofC CSV2RINEX tool demonstrates improved positioning accuracy.
- The study provides a comparative analysis of different GNSS data generation methods on Android.
Conclusions:
- The UofC CSV2RINEX tool offers a more accurate solution for GNSS data processing from smartphones.
- Addressing data quality issues in generated GNSS observations is critical for reliable positioning.
- Further collaboration with app developers is recommended to investigate and resolve GNSS data misbehavior.
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