Smartphone Positioning and Accuracy Analysis Based on Real-Time Regional Ionospheric Correction Model

Liu, Qi; Gao, Chengfa; Peng, Zihan; Zhang, Ruicheng; Shang, Rui

doi:10.3390/s21113879

Cited by 8 publications

(4 citation statements)

References 21 publications

(24 reference statements)

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“…In E5a/L5 signals the differences found are greater with the smartphone presenting values in the order of 34 dBHz, while the Alloy presents values above 46 dBHz. Normally, the difference between a smartphone and a geodetic receiver in terms of SNR is around 10 dBHz, according to several studies presented by some authors (see Liu [10], Paziewski et al [25] and Liu et al [26]), the values of 8 and 12 dBHz in the E1/L1 and E5a/L5 bands, respectively, obtained by us are in agreement with a reference value. We also see that the smartphone's minimum values are below 20 dBHz, while the receiver values are above this value.…”

Section: Signal Qualitysupporting

confidence: 89%

“…Uradzi ński & Bakuła (2020) [8] showed, using a dual frequency smartphone, accuracies of 5 cm for GPS L1 solutions using a reference station a few kilometers away with a survey time of around one hour. Other authors developed works with smartphones with other proposes, Robustelli et al [9] had done experiments to assess the observations quality and performance of GNSS standalone positioning with dual frequency smartphones, while Liu et al [10] demonstrated the impact of real-time regional ionospheric correction model can have in the smartphone positioning and Sharma et al [11] analyzed the antenna limitations in a smartphone-based GNSS positioning approaches. The authors Zangenehnejad & Gao [12] recently published a work about the opportunities and perspectives that the use of GNSS smartphones can be employed in different types of applications in the areas of mapping, navigation, cadastral, and in a wide range of works with georeferencing needs.…”

Section: Introductionmentioning

confidence: 99%

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Relative Positioning in Remote Areas Using a GNSS Dual Frequency Smartphone

Magalhães

Bastos

Maia

et al. 2021

Sensors

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The use of GPS positioning and navigation capabilities in mobile phones is present in our daily lives for more than a decade, but never with the centimeter level of precision that can actually be reached with several of the most recent smartphones. The introduction of the new GNSS systems (Global Navigation Satellite Systems), the European system Galileo, is opening new horizons in a wide range of areas that rely on precise georeferencing, namely the mass market smartphones apps. The constant growth of this market has brought new devices with innovative capabilities in hardware and software. The introduction of the Android 7 by Google, allowing access to the GNSS raw code and phase measurements, and the arrival of the new chip from Broadcom BCM47755 providing dual frequency in some smartphones came to revolutionize the positioning performance of these devices as never seen before. The Xiaomi Mi8 was the first smartphone to combine those features, and it is the device used in this work. It is well known that it is possible to obtain centimeter accuracy with this kind of device in relative static positioning mode with distances to a reference station up to a few tens of kilometers, which we also confirm in this paper. However, the main purpose of this work is to show that we can also get good positioning accuracy using long baselines. We used the ability of the Xiaomi Mi8 to get dual frequency code and phase raw measurements from the Galileo and GPS systems, to do relative static positioning in post-processing mode using wide baselines, of more than 100 km, to perform precise surveys. The results obtained were quite interesting with RMSE below 30 cm, showing that this type of smartphone can be easily used as a low-cost device, for georeferencing and mapping applications. This can be quite useful in remote areas where the CORS networks are not dense or even not available.

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Section: Signal Qualitysupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Relative Positioning in Remote Areas Using a GNSS Dual Frequency Smartphone

Magalhães

Bastos

Maia

et al. 2021

Sensors

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“…Therefore, DFRs are typically used for high-precision receivers, whereas single Frequency Receivers (SFRs) are usually utilized for mass-market GNSS receivers. Note that nowadays, dualfrequency support has been reported even in smartphones [38], yet it still does not concern the majority of smartphones today.…”

Section: Overview Of the Ionospheric Delay Conceptmentioning

confidence: 99%

“…Several things can explain this increased inaccuracy. First of all, there are many fluctuations and biases in the smartphone's components [38]. Secondly, dynamic conditions of Android-based measurements may suffer more errors compared to static acquisition.…”

Section: Data From Reference Stations-static Conditionsmentioning

confidence: 99%

Ionospheric Error Models for Satellite-Based Navigation—Paving the Road towards LEO-PNT Solutions

Imad,

Grenier,

Zhang

et al. 2023

Computers

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Low Earth Orbit (LEO) constellations have ecently gained tremendous attention in the navigational field due to their arger constellation size, faster geometry variations, and higher signal power evels than Global Navigation Satellite Systems (GNSS), making them favourable for Position, Navigation, and Timing (PNT) purposes. Satellite signals are heavily attenuated from the atmospheric ayers, especially from the ionosphere. Ionospheric delays are, however, expected to be smaller in signals from LEO satellites than GNSS due to their ower orbital altitudes and higher carrier frequency. Nevertheless, unlike for GNSS, there are currently no standardized models for correcting the ionospheric errors in LEO signals. In this paper, we derive a new model called Interpolated and Averaged Memory Model (IAMM) starting from existing International GNSS Service (IGS) data and based on the observation that ionospheric effects epeat every 11 years. Our IAMM model can be used for ionospheric corrections for signals from any satellite constellation, including LEO. This model is constructed based on averaging multiple ionospheric data and eflecting the electron content inside the ionosphere. The IAMM model’s primary advantage is its ability to be used both online and offline without needing eal-time input parameters, thus making it easy to store in a device’s memory. We compare this model with two benchmark models, the Klobuchar and International Reference Ionosphere (IRI) models, by utilizing GNSS measurement data from 24 scenarios acquired in several European countries using both professional GNSS eceivers and Android smartphones. The model’s behaviour is also evaluated on LEO signals using simulated data (as measurement data based on LEO signals are still not available in the open-access community; we show a significant eduction in ionospheric delays in LEO signals compared to GNSS. Finally, we highlight the remaining open challenges toward viable ionospheric-delay models in an LEO-PNT context.

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