Exploring GNSS Crowdsourcing Feasibility: Combinations of Measurements for Modeling Smartphone and Higher End GNSS...

Area of Science:

• Geodesy and Geomatics
• Signal Processing
• Data Science

Background:

• Crowdsourcing Global Navigation Satellite System (GNSS) receiver data offers potential for applications like weather monitoring.
• A key limitation in GNSS data crowdsourcing is the variable quality of receivers, particularly in consumer-grade devices like smartphones.
• Assessing receiver performance is critical for reliable data collection and application development.

Purpose of the Study:

• To present a method for estimating the performance of arbitrary GNSS receivers based on instrumentation-related measurement errors.
• To enable the assessment of GNSS receivers, including smartphones with integrated antennas, without requiring knowledge of the antenna's position.
• To develop a technique independent of atmospheric errors, eliminating the need for external correction services.

Main Methods:

• The proposed method utilizes receiver independent exchange format (RINEX) data.
• Performance evaluation relies on calculating error models from RINEX data, requiring only ephemeris corrections.
• The approach focuses on measurement errors intrinsic to the receiver's instrumentation.

Main Results:

• Parametrized error models were generated to represent the quality of different GNSS receiver grades.
• Smartphone GNSS receivers demonstrate typical positioning precision around the decimeter level.
• Professional-grade GNSS receivers achieve positioning precision within a few millimeters.

Conclusions:

• The developed method provides a robust way to assess GNSS receiver quality for crowdsourcing.
• The error models are valuable for stochastic modeling, such as in Kalman filters, and for evaluating receiver suitability for crowdsourcing.
• This technique facilitates the understanding and improvement of GNSS data quality from diverse sources.