Real-Time Geophysical Applications with Android GNSS Raw Measurements

Fortunato, Marco; Ravanelli, Michela; Mazzoni, Augusto

doi:10.3390/rs11182113

Cited by 35 publications

(18 citation statements)

References 17 publications

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“…In principle, all the technique presented hereafter can be extended to the case of nonstationary users, as long as that the GNSS receiver in use supports dynamic conditions, has a good clock stability, and is able to provide the observables required with good quality. There is a trend in studying ionospheric monitoring via raw measurements form smartphones, as for example in Fortunato, Ravanelli, and Mazzoni (2019) where such measurements are processed to detect traveling ionospheric disturbances (TIDs); however, only a preliminary analysis has been carried out, and it showed limitations due to the poor quality of the measurements.…”

Section: Scintillation Detectionmentioning

confidence: 99%

Survey on signal processing for GNSS under ionospheric scintillation: Detection, monitoring, and mitigation

et al. 2020

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Ionospheric scintillation is the physical phenomena affecting radio waves coming from the space through the ionosphere. Such disturbance is caused by ionospheric electron‐density irregularities and is a major threat in Global Navigation Satellite Systems (GNSS). From a signal‐processing perspective, scintillation is one of the most challenging propagation scenarios, particularly affecting high‐precision GNSS receivers and safety critical applications where accuracy, availability, continuity, and integrity are mandatory. Under scintillation, GNSS signals are affected by amplitude and phase variations, which mainly compromise the synchronization stage of the receiver. To counteract these effects, one must resort to advanced signal‐processing techniques such as adaptive/robust methods, machine learning, or parameter estimation. This contribution reviews the signal‐processing landscape in GNSS receivers, with emphasis on different detection, monitoring, and mitigation problems. New results using real data are provided to support the discussion. To conclude, future perspectives of interest to the GNSS community are discussed.

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Section: Scintillation Detectionmentioning

confidence: 99%

Survey on signal processing for GNSS under ionospheric scintillation: Detection, monitoring, and mitigation

et al. 2020

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“…More recent research demonstrates the advantage of the integration of mass-market smartphone GNSS signals with other precise sensors for geoscience applications, where accelerometers and gyroscopes are additionally used for earthquake detection or ionospheric/tropospheric disturbances [34,35].…”

Section: Use Of Gnss Carrier Phase Observations In Various Fields Of mentioning

confidence: 99%

Assessment of Static Positioning Accuracy Using Low-Cost Smartphone GPS Devices for Geodetic Survey Points’ Determination and Monitoring

Uradziński

Bakuła

2020

Applied Sciences

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Recent developments enable to access raw Global Navigation Satellite System (GNSS) measurements of mobile phones. Initially, researchers using signals gathered by mobile phones for high accuracy surveying were not successful in ambiguity fixing. Nowadays, GNSS chips, which are built in the latest smartphones, deliver code and primarily carrier phase observations available for detailed analysis in post-processing applications. Therefore, we decided to check the performance of carrier phase ambiguity fixing and positioning accuracy results of the latest Huawei P30 pro smartphone equipped with a dual-frequency GNSS receiver. We collected 3 h of raw static data in separate sessions at a known point location. For two sessions, the mobile phone was mounted vertically and for the third one—horizontally. At the same time, a high-class geodetic receiver was used for L1 and L5 signal comparison purposes. The carrier phase measurements were processed using commercial post-processing software with reference to the closest base station observations located 4 km away. Additionally, 1 h sessions were divided into 10, 15, 20 and 30 min separate sub-sessions to check the accuracy of the surveying results in fast static mode. According to the post-processing results, we were able to fix all L1 ambiguities based on Global Positioning System (GPS)-only satellite constellation. In comparison to the fixed reference point position, all three 1 h static session results were at centimeters level of accuracy (1–4 cm). For fast static surveying mode, the best results were obtained for 20 and 30 min sessions, where average accuracy was also at centimeters level.

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“…Before 2016, users only had access to the position computed by the GNSS chipsets. The release of the Android Raw GNSS Measurements API, compatible with Android N or higher, allows users to utilize the pseudorange and carrier-phase measurements to locate with their own algorithms [14]. Nonetheless, a technique aiming to prolong battery life, aka duty cycling, impedes developers to get valid carrier-phase measurements [15].…”

Section: Introductionmentioning

confidence: 99%

A Continuous Positioning Algorithm Based on RTK and VI-SLAM With Smartphones

et al. 2020

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The navigation technology has developed rapidly and immensely over the past few decades. Among the multiple navigation technologies, the representative and promising techniques are Real-time Kinematic (RTK) technique and Simultaneous Localization and Mapping (SLAM). RTK can provide realtime positioning results with high accuracy, while SLAM can not only locate the user but also construct a map of the new ambient. The first smartphone, the Xiaomi MI 8, equipped with the dual-frequency Global Navigation Satellite System (GNSS), hit the market in May 2018, providing valid carrier-phase measurements for RTK owe to the developer option of "Force full GNSS measurements." Nevertheless, RTK underperforms in urban areas as the buildings and trees can block the satellite signals. RTK cannot even provide positioning results when the GNSS outage happens. However, SLAM can effectively make up the drawbacks of RTK as it utilizes no more information than the images. SLAM can also be combined with the Inertial Measurement Unit (IMU) called Visual-Inertial SLAM (VI-SLAM,) with the improvement of accuracy and robustness. Therefore, our study mainly aims to confirm the feasibility of continuous positioning based on RTK and VI-SLAM with the Xiaomi MI 8. An application is developed to execute the functions, including logging images, measurements of IMU, and GPS measurements in Receiver INdependent EXchange (RINEX) format with the Xiaomi MI 8. The performances of RTK with and without the assistance of VI-SLAM are assessed respectively in the urban area. The experimental results demonstrate that the combination of RTK and VI-SLAM based on smartphones can effectively provide continuous positioning results. We believe our application will facilitate research and development in relation to positioning algorithms. Readers have access to this application at https://github.com/Nronaldo/CIGRLogger.

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Real-Time Geophysical Applications with Android GNSS Raw Measurements

Cited by 35 publications

References 17 publications

Survey on signal processing for GNSS under ionospheric scintillation: Detection, monitoring, and mitigation

Survey on signal processing for GNSS under ionospheric scintillation: Detection, monitoring, and mitigation

Assessment of Static Positioning Accuracy Using Low-Cost Smartphone GPS Devices for Geodetic Survey Points’ Determination and Monitoring

A Continuous Positioning Algorithm Based on RTK and VI-SLAM With Smartphones

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