Doppler effect equation variations1/8/2024 ![]() Int Arch Photogramm Remote Sens Spatial Inf Sci 89–96. Li Y, Wang L, Liu J, Zhang P, Lu Y (2022a) Accuracy evaluation of multi-GNSS Doppler velocity estimation using android smartphones. Li G, Geng J (2022) Android multi-GNSS ambiguity resolution in the case of receiver channel-dependent phase biases. Li G, Geng J (2019) Characteristics of raw multi-GNSS measurement error from Google Android smart devices. In: 2016 IEEE/ION position, location and navigation symposium (PLANS), IEEE. Humphreys TE, Murrian M, Van Diggelen F, Podshivalov S, Pesyna KM (2016) On the feasibility of cm-accurate positioning via a smartphone's antenna and GNSS chip. Håkansson M (2018) Characterization of GNSS observations from a Nexus 9 Android tablet. Geng J, Wen Q, Chen Q, Chang H (2019b) Six-degree-of-freedom broadband seismogeodesy by combining collocated high-rate GNSS, accelerometers, and gyroscopes. Geng J, Jiang E, Li G, Xin S, Wei N (2019a) An improved hatch filter algorithm towards sub-meter positioning using only android raw GNSS measurements without external augmentation corrections. Geng J, Pan Y, Li X, Guo J, Liu J, Chen X, Zhang Y (2018) Noise characteristics of high-rate multi-GNSS for subdaily crustal deformation monitoring. ![]() Geng J, Li G (2019) On the feasibility of resolving Android GNSS carrier-phase ambiguities. įreda P, Angrisano A, Gaglione S, Troisi S (2015) Time-differenced carrier phases technique for precise GNSS velocity estimation. Publications Office of the European Union, Luxembourgįortunato M, Ravanelli M, Mazzoni A (2019) Real-time geophysical applications with android GNSS raw measurements. 3932-3945ĭarugna, F (2021) Improving Smartphone-Based GNSS Positioning Using State Space Augmentation Techniques (Doctoral dissertation, Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover)Įuropean Global Navigation Satellite Systems Agency (2018) Using GNSS raw measurements on android devices-towards better location performance in mass market applications (White Paper). In: Proceedings of the 32nd international technical meeting of the satellite division of the institute of navigation. ĭarugna F, Wübbena J, Ito A, Wübbena T, Wübbena G, Schmitz, M (2019) RTK and PPP-RTK using smartphones: From short-baseline to long-baseline applications. Jet Propulsion Laboratory, pp.61–76.Ĭhen B, Gao C, Liu Y, Sun P (2019) Real-time precise point positioning with a xiaomi mi 8 android smartphone. Ĭhao CC (1974) The tropospheric calibration model for Mariner Mars 1971. īellone T, Dabove P, Manzino AM, Taglioretti C (2014) Real-time monitoring for fast deformations using GNSS low-cost receivers. In this case, there is a cost-effective solution for implementing a dense network of monitoring arrays or developing low-cost monitoring instruments with integrated GNSS.Īngrisano A, Cappello G, Del Pizzo S, Gaglione S (2022) Time-differenced carrier phase technique for precise velocity estimation on an android smartphone. These results are encouraging and show that we can obtain a few mm/s velocities using inexpensive smartphones or their embedded GNSS chipsets. In addition, the causes of this anomalous clock variation are further discussed and the noise characteristics of Android multi-constellation multi-frequency Doppler and TDCP observations are analyzed. ![]() For a representative Huawei P40 smartphone, their static experimental horizontal velocity error RMS is 0.27 and 0.26 cm/s, respectively, and their mean velocity error RMS of six shaking tests are 0.52 and 0.40 cm/s, respectively. In contrast, using the inter-satellite differencing and dual clock drift estimation strategies, the velocity error RMS are both reduced to less than 1 cm/s. The results of static and shake table experiments show that the traditional combination method solutions for smartphones contain many outliers, with the root mean square (RMS) of the horizontal velocity measurement errors exceeding 1 m/s. To solve this problem, we provide two strategies, including inter-satellite differencing and dual clock drift estimation. As a result, the traditional Doppler and TDCP combination method that estimates the receiver TDCP clock drift as the same parameter as the Doppler clock drift is no longer applicable. ![]() Based on the smartphone GNSS data generated by the Geo + + RINEX Logger, we found smartphone anomalous clock variations, as evidenced by the biases between the TDCP-estimated and Doppler-estimated receiver clock drifts, as well as frequent jumps. In this study, the Android GNSS velocity measurement performance of mass-market smartphones was evaluated. The increasingly improved performance of mass-market GNSS chipsets is driving smartphone GNSS positioning or velocimetry as a low-cost GNSS solution for high-precision vibration monitoring applications. ![]()
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