Abstract
Low-cost sensor navigation has risen in the past decade with the onset of many modern applications that demand decimeter-level accuracy using mass-market sensors. The key advantage of the precise pointing positioning (PPP) technique over real-time kinematic (RTK) is the non-requirement of local infrastructure and still being able to attain decimeter to sub-meter level accuracy while using mass-market low-cost sensors. Achieving decimeter to sub-meter-level accuracy is a challenge in urban environments. Therefore, adaptive filtering for low-cost sensors is necessary along with motion-based constraining and atmosphere constraints. The traditional robust adaptive Kalman filter (RAKF) uses empirical limits that are derived by analyzing the GNSS receiver data learning statistics based on confidence intervals beforehand to determine when the adaptive factor needs to be applied. In this research, a new technique is proposed to determine the adaptive factor computation based on the detection of an increase in the number of satellite signals after a partial outage. The proposed method provides 6–46% better accuracy than the traditional RAKF and 11–55% better accuracy performance when compared to a tightly coupled solution without enhancements when multiple datasets were tested. The results prove to be a significant improvement for the next generation of applications, such as low-autonomous and intelligent transportation systems.
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The data used in this research were collected by sensors owned by GNSS Lab, York University, Canada. The data are not available publicly currently. If a person or a party is interested in using the data, they can reach out to the corresponding author.
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Acknowledgements
The authors would like to thank the Natural Sciences and Engineering Research Council (NSERC) and York University for providing funding for this research and the Centre National d’Etudes Spatiales (CNES) for data. The authors would also like to thank GNSS Lab, York University members for data collection and other supportive activities.
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Funding for this research was provided by the National Science and Engineering Research Council of Canada (NSERC) and York University.
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SV performed the processing and analysis and wrote the manuscript. SB led the development of the paper and provided advice and guidance for the experimental design, analysis and paper writing, and reviewed and edited the manuscript. Both authors reviewed the paper and agreed to the submitted version.
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Vana, S., Bisnath, S. A modified adaptive factor-based Kalman filter for continuous urban navigation with low-cost sensors. GPS Solut 28, 84 (2024). https://doi.org/10.1007/s10291-023-01606-2
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DOI: https://doi.org/10.1007/s10291-023-01606-2