Assessment of GNSS signal strength and positioning performance using single/dual-frequency android smartphones
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Devadas Kuna
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Naveen Kumar Perumalla
Abstract
The integration of Global Navigation Satellite System (GNSS) chips in commercial smartphones has enabled cost-effective geolocation and navigation applications. Modern GNSS-enabled smartphones can record raw GNSS data using freely available Android apps, making them viable for geodetic applications without additional hardware or software costs. Despite their advantages, challenges such as noisy GNSS observations, environmental effects, and device-holding modes limit the accuracy of smartphone-based positioning. This study investigates the feasibility of using low-cost, single/dual-frequency Android smartphones (Huawei Nova 3e, Moto G52, and Poco M3) for acquiring raw GNSS observations in indoor and outdoor environments, with a focus on signal strength and positioning precision (with respect to the median solution). Dual-frequency linear combinations (L1/E1, L5/E5) were used to reduce first-order ionospheric delays to improve relative positioning precision. The study further analyzes the performance of Weighted Least Squares (WLS) and Kalman Filtering (KF) for smartphone-based positioning. Results highlight the benefits of KF for dual-frequency smartphones and reveal that satellite acquisition primarily depends on the GNSS chipset used. These findings provide valuable insights for the geospatial and GNSS research community, demonstrating the potential of GNSS smartphones for cost-effective and practical applications.
Acknowledgments
The authors would like to thank, Advanced GNSS Research Laboratory (AGRL), Department of Electronics and Communication Engineering of Osmania University College of Engineering for providing the facilities to carry out this research work. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: Devadas Kuna and Naveen Kumar Perumalla contributed to the planning and execution of the current study, as well as to the results analysis and manuscript writing. The manuscript has been read and approved by the authors.
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Use of Large Language Models, AI and Machine Learning Tools: Not applicable.
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Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Research funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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Data availability: The data sets used and analysed in this study are not publicly available, but the researchers are willing to provide them upon request.
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