Study on Multi-GNSS Precise Point Positioning Performance with Adverse Effects of Satellite Signals on Android Smartphone

Zhu, Hongyu; Xia, Lan; Wu, Dongjin; Xia, Jingchao; Li, Qianxia

doi:10.3390/s20226447

Cited by 17 publications

(12 citation statements)

References 32 publications

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“…For the 3D position, the improvement can reach 22.7% and 24.2% over the elevation-angle-only and C/N0-only weighting scenarios, respectively. The obtained PPP accuracy is comparable to the existing research [ 12 , 13 ].…”

Section: Resultssupporting

confidence: 84%

“…In addition, it is more prone to reconvergence under the constrained satellite visibility condition, which is especially apparent in the up direction. The achieved PPP accuracy is comparable to the existing research [ 12 , 13 ].…”

Section: Resultssupporting

confidence: 80%

“…In addition, using the Xiaomi MI8 smartphone, Shinghal and Bisnath [ 11 ] utilized the C/N0-dependent stochastic model to improve the static PPP three-dimensional (3D) accuracy by about 27.0% over the elevation-angle-dependent stochastic model. Zhu et al [ 12 ] used a Huawei Mate 30 smartphone to test the PPP in a kinematic mode based on the C/N0-dependent stochastic model to achieve a positioning accuracy of 0.93 m, 0.62 m and 2.17 m in the east, north and up directions, respectively; the accuracy was improved by about 26.2%, 20.5% and 20.4% when compared with the elevation-angle-dependent stochastic model. From the existing research, the advantage of the C/N0-dependent stochastic model is obvious for smartphone-based GNSS positioning.…”

Section: Introductionmentioning

confidence: 99%

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A Combined Elevation Angle and C/N0 Weighting Method for GNSS PPP on Xiaomi MI8 Smartphones

Cai

2022

Sensors

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Traditionally, an elevation-angle-dependent weighting method is usually used for Global Navigation Satellite System (GNSS) positioning with a geodetic receiver. As smartphones adopt linearly polarized antenna and low-cost GNSS chips, different GNSS observation properties are exhibited. As a result, a carrier-to-noise ratio (C/N0)-dependent weighting method is mostly used for smartphone-based GNSS positioning. However, the C/N0 is subject to the effects of the observation environment, resulting in an unstable observation weight. In this study, we propose a combined elevation angle and C/N0 weighting method for smartphone-based GNSS precise point positioning (PPP) by normalizing the C/N0-derived variances to the scale of the elevation-angle-derived variances. The proposed weighting method is validated in two kinematic PPP tests with different satellite visibility conditions. Compared with the elevation-angle-only and C/N0-only weighting methods, the combined weighting method can effectively enhance the smartphone-based PPP accuracy in a three-dimensional position by 22.7% and 24.2% in an open-sky area, and by 52.0% and 26.0% in a constrained visibility area, respectively.

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Section: Resultssupporting

confidence: 84%

Section: Resultssupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

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A Combined Elevation Angle and C/N0 Weighting Method for GNSS PPP on Xiaomi MI8 Smartphones

Cai

2022

Sensors

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“…Such progress induced the scientific community to draw special attention to GNSS observations derived from smartphones. As a result, a great deal of effort has been put into the development of observational and stochastic models that are suited to process smartphone GNSS observations [ 6 , 7 ]. These algorithms address the specific limitations of smartphone GNSS observations such as the low suppression to multipath and high observational noise highlighted by Riley et al [ 8 ], the carrier phase discontinuity that is driven by duty-cycle analyzed by Paziewski et al [ 9 ] and an existence of unwanted biases that destroy integer properties of phase ambiguities showed by Humphreys et al [ 10 ] and Li and Geng [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Observation Quality Assessment and Performance of GNSS Standalone Positioning with Code Pseudoranges of Dual-Frequency Android Smartphones

Robustelli

Paziewski

Pugliano

2021

Sensors

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The new generation of Android smartphones is equipped with GNSS chips capable of tracking multi-frequency and multi-constellation data. In this work, we evaluate the positioning performance and analyze the quality of observations collected by three recent smartphones, namely Xiaomi Mi 8, Xiaomi Mi 9, and Huawei P30 pro that take advantage of such chips. The analysis of the GNSS observation quality implies that the commonly employed elevation-dependent function is not optimal for smartphone GNSS observation weighting and suggests an application of the C/N0-dependent one. Regarding smartphone code signals on L5 and E5a frequency bands, we found that they are characterized with noticeably lower noise as compared to E1 and L1 ones. The single point positioning results confirm an improvement in the performance when the weights are a function of the C/N0-rather than those dependent on the satellite elevation and that a smartphone positioning with E5a code observations significantly outperforms that with E1 signals. The latter is expressed by a drop of the horizontal RMS from 8.44 m to 3.17 m for Galileo E1 and E5a solutions of Xiaomi Mi 9 P30, respectively. The best positioning accuracy of multi-GNSS single-frequency (L1/E1/B1/G1) solution was obtained by Huawei P30 with a horizontal RMS of 3.24 m. Xiaomi Mi 8 and Xiaomi Mi 9 show a horizontal RMS error of 4.14 m and 4.90 m, respectively.

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“…For the convergence of the smartphone GNSS with various sensors, such as inertial measurement units including global positioning systems (GPSs), research related to location accuracy improvement using communication networks and WiFi is being actively conducted. (12)(13)(14) In this study, we propose a relative and clustering analysis correction (RCC) technique to improve positioning accuracy via multipath error cancelation through relative positioning between the smartphone and Android GNSS signal characteristic values using only smartphone GNSS signals without external communication or equipment. The effectiveness of this technique is verified by comparison with the results of point positioning.Recently, with the development of new devices, such as smartphones, wearable devices, drones, and autonomous vehicles, the demand for high-precision location information from lowcost GNSS is increasing.…”

mentioning

confidence: 99%

Improving Positional Accuracy Using Relative Measurement between Android Smartphones

Jang¹,

Kim²,

Jung³

et al. 2022

Sensors and Materials

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In this study, we propose a relative and clustering analysis correction (RCC) technique capable of improving the location accuracy of a smartphone global navigation satellite system (GNSS). The RCC technique improves the accuracy of the Android GNSS by eliminating common error components from pseudoscope measurements as well as noncommon errors through cluster analysis using Android GNSS signal attributes. Cluster analysis was applied to the RCC technique using the optimal clustering method among the hierarchical clustering, K-means clustering, and neural network clustering methods. As a result of verifying the RCC technique, the following results were obtained. The distance error of a zero-baseline experiment, which was performed to check the relative accuracy and precision between smartphone GNSSs, was 0.572 m for two sessions, which showed that the noise-causing error of the Android smartphone GNSS used in the experiment occurred similarly in each session. Positioning accuracy was much lower in a multipath environment than in an open environment due to the reflection and refraction of satellite signals by obstacles, such as buildings around the receiver and multipath generation due to low-elevation non-line-of-sight satellite signals. However, observations confirmed that applying the RCC technology to the Android smartphone GNSS with errors of more than 5 m in multipath environments can secure high location accuracy, even in multipath environments.Recent smartphones have been equipped with GNSS chipsets, and location information can be obtained through an application programming interface (API). (1)(2)(3) In May 2016, Google announced that it would provide GNSS raw data for smartphones and tablets that support the Android 7.0 (Android Nougat) operating system. Smartphones equipped with GNSS chipsets running Android 7.0 or later can calculate not only a user's location information (latitude, longitude, and elevation) but also the pseudodistance between the satellite and receiver as well as provide a direct signal timestamp that allows the user's location to be calculated. Notably, GNSS raw data are now available. (4)(5)(6) In particular, the first dual-frequency GNSS chipset was released in June 2018 in a smartphone equipped with a BCM47755 location hub, which increased the availability of satellite signals and improved positioning accuracy. The provision of such GNSS raw data enabled the design of an advanced positioning algorithm and improved accuracy in a usercentered location-based service (LBS). (7,8) Currently, programs and applications that can use GNSS raw data are being developed, and various studies related to the high positioning accuracy of smartphones are being conducted. For high-precision geodetic applications, precise point positioning (PPP) is well known to provide high positioning accuracy as evidenced in various research contributions. (6) The use of the dualfrequency ionosphere free linear combination has typically defined conventional GNSS PPP processing. (8) However, owing to satellite mod...

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Study on Multi-GNSS Precise Point Positioning Performance with Adverse Effects of Satellite Signals on Android Smartphone

Cited by 17 publications

References 32 publications

A Combined Elevation Angle and C/N0 Weighting Method for GNSS PPP on Xiaomi MI8 Smartphones

A Combined Elevation Angle and C/N0 Weighting Method for GNSS PPP on Xiaomi MI8 Smartphones

Observation Quality Assessment and Performance of GNSS Standalone Positioning with Code Pseudoranges of Dual-Frequency Android Smartphones

Improving Positional Accuracy Using Relative Measurement between Android Smartphones

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