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Relevant bibliographies by topics / Android GNSS measurements

Academic literature on the topic 'Android GNSS measurements'

Author: Grafiati

Published: 14 March 2023

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Journal articles on the topic "Android GNSS measurements"

1

Skorupa, Bogdan. "The problem of GNSS positioning with measurements recorded using Android mobile devices." Budownictwo i Architektura 18, no. 3 (January 24, 2020): 051–62. http://dx.doi.org/10.35784/bud-arch.738.

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The current work presents the issue of determining the position of the observer using measurements registered with GNSS (Global Navigation Satellite System) receivers that Android mobile devices are equipped with. The discussed questions concern using GNSS measurement data, which have been made available in the Android system since version 7.0. The present paper has the character of a review. It demonstrates how measurement data can be obtained via Application Programming Interface. Moreover, it discusses the available software that can be for registering measurements and their initial analysis. Subsequently, it reviews scientific works concerning the problem of positioning with the use of smartphones. Special emphasis was placed on tests consisting in an analysis of phase observations registered using dual-frequency receivers. The summary of the article presents the prospects for using mobile devices in precise point positioning. It also points out the limitations to achieving high accuracy and reliability of such measurements.

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2

Fortunato, Marco, Michela Ravanelli, and Augusto Mazzoni. "Real-Time Geophysical Applications with Android GNSS Raw Measurements." Remote Sensing 11, no. 18 (September 11, 2019): 2113. http://dx.doi.org/10.3390/rs11182113.

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The number of Android devices enabling access to raw GNSS (Global Navigation Satellite System) measurements is rapidly increasing, thanks to the dedicated Google APIs. In this study, the Xiaomi Mi8, the first GNSS dual-frequency smartphone embedded with the Broadcom BCM47755 GNSS chipset, was employed by leveraging the features of L5/E5a observations in addition to the traditional L1/E1 observations. The aim of this paper is to present two different smartphone applications in Geoscience, both based on the variometric approach and able to work in real time. In particular, tests using both VADASE (Variometric Approach for Displacement Analysis Stand-alone Engine) to retrieve the 3D velocity of a stand-alone receiver in real-time, and VARION (Variometric Approach for Real-Time Ionosphere Observations) algorithms, able to reconstruct real-time sTEC (slant total electron content) variations, were carried out. The results demonstrate the contribution that mass-market devices can offer to the geosciences. In detail, the noise level obtained with VADASE in a static scenario—few mm/s for the horizontal components and around 1 cm/s for the vertical component—underlines the possibility, confirmed from kinematic tests, of detecting fast movements such as periodic oscillations caused by earthquakes. VARION results indicate that the noise level can be brought back to that of geodetic receivers, making the Xiaomi Mi8 suitable for real-time ionosphere monitoring.

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3

Weng, Duojie, Xingli Gan, Wu Chen, Shengyue Ji, and Yangwei Lu. "A New DGNSS Positioning Infrastructure for Android Smartphones." Sensors 20, no. 2 (January 15, 2020): 487. http://dx.doi.org/10.3390/s20020487.

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One’s position has become an important piece of information for our everyday lives in a smart city. Currently, a position can be obtained easily using smartphones that is equipped with low-cost Global Navigation Satellite System (GNSS) chipsets with accuracy varying from 5 m to 10 m. Differential GNSS (DGNSS) is an efficient technology that removes the majority of GNSS errors with the aid of reference stations installed at known locations. The sub-meter accuracy can be achieved when applying the DGNSS technology on the advanced receivers. In 2016, Android has opened the accesses of raw GNSS measurements to developers. However, most of the mid and low-end smartphones only provide the data using the National Marine Electronics Association (NMEA) protocol. They do not provide the raw measurements, and thus do not support the DGNSS operation either. We proposed a DGNSS infrastructure that correct the standalone GNSS position of smartphones using the corrections from the reference station. In the infrastructure, the position correction is generated considering the GNSS satellite IDs that contribute to the standalone solution in smartphones, and the position obtained is equivalent to the solution of using the range-domain correction directly. To serve a large number of smartphone users, a Client/Server architecture is developed to cope with a mass of DGNSS positioning requests efficiently. The comparison of the proposed infrastructure against the ground truth, for all field tests in open areas, showed that the infrastructure achieves the horizontal positioning accuracy better than 2 m. The improvement in accuracy can reach more than 50% for the test in the afternoon. The infrastructure brings benefits to applications that require more accuracy without requiring any hardware modifications.

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Yun, Jeonghyeon, Cheolsoon Lim, and Byungwoon Park. "Inherent Limitations of Smartphone GNSS Positioning and Effective Methods to Increase the Accuracy Utilizing Dual-Frequency Measurements." Sensors 22, no. 24 (December 15, 2022): 9879. http://dx.doi.org/10.3390/s22249879.

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Xiaomi Mi8 with a Broadcom BCM47755 chip, an Android smartphone that supports multi-constellation (GPS, GLONASS, Galileo, BeiDou, and QZSS) and dual-frequency (L1/E1 and L5/E5), was launched in May 2018. Unlike previously released smartphones, it was technically expected to provide robust precise positioning with a fast ambiguity resolution, which led many researchers to be overly optimistic about the applicability of high-accuracy techniques such as real-time kinematic (RTK) systems and precise point positioning (PPP) of smartphones. The global navigation satellite system (GNSS) raw measurement quality of Android smartphones is, however, inherently far lower than that of general GNSS receivers due to their structure, which accordingly makes it difficult for them to be realized. Considering inherent limitations of smartphones such as low-quality antenna, frequent cycle slips, and the duty cycle, a practical strategy including L5 measurements, pseudo-range corrections for L5, and a weighting method is proposed in this paper. The results show that the proposed methods of L5 differential GNSS (DGNSS) and Doppler-based filtering can guarantee a positioning accuracy of 1.75 m horizontally and 4.56 m vertically in an Android device, which is comparable to the performance of commercial low-cost receivers.

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Gogoi, Neil, Alex Minetto, Nicola Linty, and Fabio Dovis. "A Controlled-Environment Quality Assessment of Android GNSS Raw Measurements." Electronics 8, no. 1 (December 21, 2018): 5. http://dx.doi.org/10.3390/electronics8010005.

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Raw Global Navigation Satellite System (GNSS) measurements have been available since 2016 in select Android smartphones. The availability of such observations allows smartphones users, in principle, to significantly improve the quality of GNSS-based positioning by applying customized and advanced positioning algorithms. However, the quality of such measurements is poor, mainly because of the low quality of smartphone hardware components and the nonideal environment in which phones are typically used. To overcome this problem and to separate the contribution of the hardware components and signal quality, dedicated test campaigns were carried out in a real environment and in a controlled-environment anechoic chamber using several different Android models. In addition, signal-processing techniques aimed at increasing the accuracy and precision of the solution were employed. Results show that the quality of the data captured in the anechoic chamber was significantly better than in real conditions. Furthermore, such analysis allows to underline certain phenomena in smartphones, such as the duty cycle, and to test the validity of anechoic environments for Android raw measurements.

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6

Benvenuto, Lorenzo, Paolo Dabove, Ilaria Ferrando, and Domenico Sguerso. "Preliminary Results on Tropospheric ZTD Estimation by Smartphone." Remote Sensing 13, no. 22 (November 13, 2021): 4567. http://dx.doi.org/10.3390/rs13224567.

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The Global Navigation Satellite System (GNSS) receiver is one of the many sensors embedded in smartphones. The early versions of the Android operating system could only access limited information from the GNSS, allowing the related Application Program Interface (API) to obtain only the location. With the development of the Android 7.0 (Nougat) operating system in May 2016, raw measurements from the internal GNSS sensor installed in the smartphone could be accessed. This work aims to show an initial analysis regarding the feasibility of Zenith Total Delay (ZTD) estimation by GNSS measurements extracted from smartphones, evaluating the accuracy of estimation to open a new window on troposphere local monitoring. Two different test sites have been considered, and two different types of software for data processing have been used. ZTDs have been estimated from both a dual-frequency and a multi-constellation receiver embedded in the smartphone, and from a GNSS Continuously Operating Reference Station (CORS). The results have shown interesting performances in terms of ZTD estimation from the smartphone in respect of the estimations obtained with a geodetic receiver.

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Everett, Tim, Trey Taylor, Dong-Kyeong Lee, and Dennis M. Akos. "Optimizing the Use of RTKLIB for Smartphone-Based GNSS Measurements." Sensors 22, no. 10 (May 18, 2022): 3825. http://dx.doi.org/10.3390/s22103825.

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The Google Smartphone Decimeter Challenge (GSDC) was a competition held in 2021, where data from a variety of instruments useful for determining a phone’s position (signals from GPS satellites, accelerometer readings, gyroscope readings, etc.) using Android smartphones were provided to be processed/assessed in regard to the most accurate determination of the longitude and latitude of user positions. One of the tools that can be utilized to process the GNSS measurements is RTKLIB. RTKLIB is an open-source GNSS processing software tool that can be used with the GNSS measurements, including code, carrier, and doppler measurements, to provide real-time kinematic (RTK), precise point positioning (PPP), and post-processed kinematic (PPK) solutions. In the GSDC, we focused on the PPK capabilities of RTKLIB, as the challenge only required post-processing of past data. Although PPK positioning is expected to provide sub-meter level accuracies, the lower quality of the Android measurements compared to geodetic receivers makes this performance difficult to achieve consistently. Another latent issue is that the original RTKLIB created by Tomoji Takasu is aimed at commercial GNSS receivers rather than smartphones. Therefore, the performance of the original RTKLIB for the GSDC is limited. Consequently, adjustments to both the code-base and the default settings are suggested. When implemented, these changes allowed RTKLIB processing to score 5th place, based on the performance submissions of the prior GSDC competition. Detailed information on what was changed, and the steps to replicate the final results, are presented in the paper. Moreover, the updated code-base, with all the implemented changes, is provided in the public repository. This paper outlines a procedure to optimize the use of RTKLIB for Android smartphone measurements, highlighting the changes needed given the low-quality measurements from the mobile phone platform (relative to the survey grade GNSS receiver), which can be used as a basis point for further optimization for future GSDC competitions.

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Hu, Jiahuan, Ding Yi, and Sunil Bisnath. "A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments." Sensors 23, no. 3 (February 2, 2023): 1631. http://dx.doi.org/10.3390/s23031631.

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Precise positioning using smartphones has been a topic of interest especially after Google decided to provide raw GNSS measurement through their Android platform. Currently, the greatest limitations in precise positioning with smartphone Global Navigation Satellite System (GNSS) sensors are the quality and availability of satellite-to-smartphone ranging measurements. Many papers have assessed the quality of GNSS pseudorange and carrier-phase measurements in various environments. In addition, there is growing research in the inclusion of a priori information to model signal blockage, multipath, etc. In this contribution, numerical estimation of actual range errors in smartphone GNSS precise positioning in realistic environments is performed using a geodetic receiver as a reference. The range errors are analyzed under various environments and by placing smartphones on car dashboards and roofs. The distribution of range errors and their correlation to prefit residuals is studied in detail. In addition, a comparison of range errors between different constellations is provided, aiming to provide insight into the quantitative understanding of measurement behavior. This information can be used to further improve measurement quality control, and optimize stochastic modeling and position estimation processes.

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Dabove, Paolo, Vincenzo Di Pietra, and Marco Piras. "GNSS Positioning Using Mobile Devices with the Android Operating System." ISPRS International Journal of Geo-Information 9, no. 4 (April 7, 2020): 220. http://dx.doi.org/10.3390/ijgi9040220.

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The access and the use of the global navigation satellite system (GNSS) pseudo-range and carrier-phase measurements mobile devices as smartphones and tablets with an Android operating system has transformed the concept of accurate positioning with mobile devices. In this work, the comparison of positioning performances obtained with a smartphone and an external mass-market GNSS receiver both in real-time and post-processing is made. Particular attention is also paid to accuracy and precision of positioning results, also analyzing the possibility of estimating the phase ambiguities as integer values (fixed positioning) that it is still challenging for mass-market devices. The precisions and accuracies obtained with the mass-market receiver were about 5 cm and 1 cm both for real-time and post-processing solutions, respectively, while those obtained with a smartphone were slightly worse (few meters in some cases) due to the noise of its measurements.

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10

Chen, Bo, Chengfa Gao, Yongsheng Liu, and Puyu Sun. "Real-time Precise Point Positioning with a Xiaomi MI 8 Android Smartphone." Sensors 19, no. 12 (June 25, 2019): 2835. http://dx.doi.org/10.3390/s19122835.

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The Global Navigation Satellite System (GNSS) positioning technology using smartphones can be applied to many aspects of mass life, and the world’s first dual-frequency GNSS smartphone Xiaomi MI 8 represents a new trend in the development of GNSS positioning technology with mobile phones. The main purpose of this work is to explore the best real-time positioning performance that can be achieved on a smartphone without reference stations. By analyzing the GNSS raw measurements, it is found that all the three mobile phones tested have the phenomenon that the differences between pseudorange observations and carrier phase observations are not fixed, thus a PPP (precise point positioning) method is modified accordingly. Using a Xiaomi MI 8 smartphone, the modified real-time PPP positioning strategy which estimates two clock biases of smartphone was applied. The results show that using multi-GNSS systems data can effectively improve positioning performance; the average horizontal and vertical RMS positioning error are 0.81 and 1.65 m respectively (using GPS, BDS, and Galileo data); and the time required for each time period positioning errors in N and E directions to be under 1 m is less than 30s.

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Dissertations / Theses on the topic "Android GNSS measurements"

1

GOGOI, NEIL. "Robust Low-Cost Navigation Solutions for Service Robotics." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2960749.

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Book chapters on the topic "Android GNSS measurements"

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Zhang, Kaishi, Fangtan Jiao, and Jianwen Li. "The Assessment of GNSS Measurements from Android Smartphones." In Lecture Notes in Electrical Engineering, 147–57. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0029-5_14.

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Conference papers on the topic "Android GNSS measurements"

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Shade, Steven, and Premal H. Madhani. "Android GNSS Measurements - Inside the BCM47755." In 31st International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2018). Institute of Navigation, 2018. http://dx.doi.org/10.33012/2018.16001.

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Liu, Jiyuan, Yifan Hu, Dongsong Zhang, and Huafu Liu. "Performance assessment of GNSS measurements from Android platform." In 2017 6th International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2017. http://dx.doi.org/10.1109/iccsnt.2017.8343742.

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VERHEYDE, Thomas, Antoine BLAIS, Christophe MACABIAU, and Francois-Xavier MARMET. "Analyzing Android GNSS Raw Measurements Flags Detection Mechanisms for Collaborative Positioning in Urban Environment." In 2020 International Conference on Localization and GNSS (ICL-GNSS). IEEE, 2020. http://dx.doi.org/10.1109/icl-gnss49876.2020.9115564.

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4

Psychas, Dimitrios, Jon Bruno, Lotfi Massarweh, and Francesco Darugna. "Towards Sub-meter Positioning using Android Raw GNSS Measurements." In 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019). Institute of Navigation, 2019. http://dx.doi.org/10.33012/2019.17077.

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Miralles, Damian, Maxim S. Moghadam, and Dennis M. Akos. "GNSS Threat Monitoring and Reporting with the Android Raw GNSS Measurements and STRIKE3." In 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019). Institute of Navigation, 2019. http://dx.doi.org/10.33012/2019.16984.

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Gogoi, Neil, Alex Minetto, and Fabio Dovis. "On the Cooperative Ranging between Android Smartphones Sharing Raw GNSS Measurements." In 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall). IEEE, 2019. http://dx.doi.org/10.1109/vtcfall.2019.8891320.

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Navarro-Gallardo, Moises, Nils Bernhardt, Michael Kirchner, Justyna Redenkiewicz Musial, and Martin Sunkevic. "Assessing Galileo Readiness in Android Devices Using Raw Measurements." In 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017). Institute of Navigation, 2017. http://dx.doi.org/10.33012/2017.15183.

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Shin, Donghyun, Cheolsoon Lim, Byungwoon Park, Youngsun Yun, Euiho Kim, and Changdon Kee. "Single-Frequency Divergence-free Hatch Filter for the Android N GNSS Raw Measurements." In 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017). Institute of Navigation, 2017. http://dx.doi.org/10.33012/2017.15360.

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Miralles, Damian, Nathan Levigne, Dennis M. Akos, Juan Blanch, and Sherman Lo. "Android Raw GNSS Measurements as the New Anti-Spoofing and Anti-Jamming Solution." In 31st International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2018). Institute of Navigation, 2018. http://dx.doi.org/10.33012/2018.15883.

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10

Miralles, Damian, Dennis M. Akos, Dong-Kyeong Lee, Andriy Konovaltsev, Lothar Kurz, and Sherman Lo. "Robust Satellite Navigation in the Android Operating System using the Android Raw GNSS Measurements Engine and Location Providers." In 2020 European Navigation Conference (ENC). IEEE, 2020. http://dx.doi.org/10.23919/enc48637.2020.9317434.

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