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Three-dimensional coordinate error histogram for Galileo single point positioning of Xiaomi Mi 9. The results achieved with with E1 code observations are represented in red while those obtained with E5a ones are represented in blue. Each bin is 1 m wide.
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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 c...
Citations
... The root mean square error of these measurements was at the level of 4.57 m. The paper [21] presents the results of a multi-GNSS single-frequency (L1/E1/B1/G1) positioning solution obtained by Huawei P30 with a horizontal RMS of 3.24 m. On the other hand, the use of raw GNSS data on smartphones for DGNSS positioning can yield accuracy results of 1.5 m [22]. ...
The potential for the use of smartphones in GNSSs (Global Navigation Satellite Systems) positioning has increased in recent years due to the emergence of the ability of Android-based devices used to process raw satellite data. This paper presents the results of a study on the use of SBAS data transmitted by the EGNOS (European Geostationary Navigation Overlay Service) system in GNSS positioning using a Xiaomi Mi8 smartphone. Raw data recorded at a fixed point were used in post-processing calculations in GPS/EGNOS positioning by determining the coordinates for every second of a session of about 5 h and comparing the results to those obtained with a Septentrio AsteRx2 GNSS receiver operating at the same time at a distance of about 3 m. The calculations were performed using the assumptions of the GNSS/SBAS positioning algorithms, which were modified with carrier-phase smoothed code observations and the content of the corrections transmitted by EGNOS.
... Other smartphones than the ones used in this study have been subject to testing (e.g. Xiaomi Mi 8 [7,8]; Xiaomi Mi 9, Huawei P30: [9]) in regards to positioning performance. Looking at static solutions for the Huawei P30 [10], one can see that code solutions reached 3Daccuracies (RMSE) of 6.3 m for 5 min observations and 1.9 m for 60 min observations. ...
This study presents an Android-based cooperative positioning (CP) architecture to improve the GNSS positioning performance on mobile devices. SBAS (Satellite Based Augmentation System) augmentation increases positioning accuracies significantly by sharing corrections between SBAS-enabled and non-capable devices via wireless connection or using a central server. The Indian GAGAN (GPS Aided GEO Augmented Navigation) is employed and assessed in the experiments. If GAGAN corrections are applied, all three chosen mobile devices showed a positioning accuracy improvement of around 95 %. The average 2D RSME was reduced from 75.23 to 1.35 m for the single-frequency GNSS smartphone Xiaomi Redmi Note 8 and from 33.25 to 1.62 m for the dual-frequency Google Pixel 4. As expected, the third GIS mapping device, Stonex S70 tablet, showed the highest performance, achieving sub-meter positioning accuracies. Thus, the experiment has proven the suitability of GAGAN augmentation for mobile devices, providing positive insight for further development of the CP architecture.
... Other smartphones than the ones used in this study have been subject to testing (e.g. Xiaomi Mi 8 [7,8]; Xiaomi Mi 9, Huawei P30: [9]) in regards to positioning performance. Looking at static solutions for the Huawei P30 [10], one can see that code solutions reached 3Daccuracies (RMSE) of 6.3 m for 5 min observations and 1.9 m for 60 min observations. ...
Mobile devices are now powerful tools for an increasing number of applications in the field of geomatics. In this study the application of SBAS (Satellite Based Augmentation System) correction data on measurements of smart mobile devices is investigated to improve the performance of positioning capabilities in terms of accuracy and precision of the solution. For that purpose, a correction model is developed employing SBAS correction data on both single- and dual-frequency multi-constellation Android-based smart mobile devices. A comparison of the smart mobile devices’ results was made to two geodetic grade GNSS receivers. As the observations in this study with these devices were carried out in Sri Lanka, the Indian GAGAN (GPS Aided Geo Augmented Navigation) SBAS is used for the correction model derivation. It must be noted, however, that the developed approach is capable of working with any SBAS worldwide. Before the SBAS correction application on the observations, the achieved Root Mean Square Error (RMSE) was around 75.2 and 33.3 m on average on single and dual-frequency smartphones, respectively. However, after applying the SBAS correction, all devices improved performance. The RMSE values improved for all devices, with the Stonex 900A geodetic receiver having the lowest value (0.42 m), followed by the Leica GS15 (0.49 m), the Android Stonex S70 GIS tablet (1.09 m), the Xiaomi Redmi Note 8 single-frequency smartphone (1.35 m), and the Google Pixel 4 dual-frequency mobile device (1.62 m). Similarly, the standard deviation (STD) values also improved, with the Stonex 900A and Leica GS15 having the lowest values. The SBAS correction also reduced the size of the confidence error ellipses for all smart devices. After SBAS correction, even the single-frequency carrier-phase smartphone Redmi Note 8 showed a significant reduction in SD and RMSE values (98% improvement), which surpassed the performance of the Pixel 4 in standalone mode. This can be attributed to SBAS correction relying solely on the L1 frequency signal and correction (such as in the GAGAN augmentation), independent of the L2 or L5 GPS frequency. Additionally, differences in hardware and software between devices can impact GNSS performance, including antenna design, processing algorithms, and signal filtering techniques, all influencing GNSS accuracy and reliability.
... Currently, few studies have been performed to analyze the performance of TDCP using such devices, which are usually characterized by noisy and discontinuous measurements. The measurement quality of such devices was analyzed and assessed in several studies; in [22][23][24], exhaustive assessments of several smartphones' observations are provided in terms of different aspects: carrier-to-noise density power ratio (C/N0), multipath error, positioning performance, and so on. The TDCP potentialities of such devices are also revealed; in [25], a static analysis considering only GPS data was conducted by comparing receivers of different grades, confirming mm/s level accuracies for all the adopted devices, i.e., for the smartphone as well. ...
Velocity estimation has a key role in several applications; for instance, velocity estimation in navigation or in mobile mapping systems and GNSSs is currently a common way to achieve reliable and accurate velocity. Two approaches are mainly used to obtain velocity based on GNSS measurements, i.e., Doppler observations and carrier phases differenced in time (that is, TDCP). In a benign environment, Doppler-based velocity can be estimated accurately to within a few cm/s, while TDCP-based velocity can be estimated accurately to within a few mm/s. On the other hand, the TDCP technique is more prone to availability shortage and the presence of blunders. In this work, the two mentioned approaches are tested, using three devices of different grades: a high-grade geodetic receiver, a high-sensitivity receiver, and a GNSS chip mounted on a smartphone. The measurements of geodetic receivers are inherently cleaner, providing an accurate solution, while the remaining two receivers provide worse results. The case of smartphone GNSS chips can be particularly critical owing to the equipped antenna, which makes the measurements noisy and largely affected by blunders. The GNSSs are considered separately in order to assess the performance of the single systems. The analysis carried out in this research confirms the previous considerations about receiver grades and processing techniques. Additionally, the obtained results highlight the necessity of adopting a diagnostic approach to the measurements, such as RAIM-FDE, especially for low-grade receivers.
... However, it should be noted that the Samsung S23 series, equipped with the same chipset, only records code observations, failing to fully utilize the dual-frequency capabilities. The accuracy of the raw GNSS measurements from the Xiaomi Mi8 and Mi9 models was assessed in the studies conducted by Chen et al. 2019 andRobustelli et al. 2021. These studies assumed that the duty cycle had no influence on the operator phase and that the L5/E5 frequency observations exhibited higher quality compared to the L1/E1 frequency data. ...
GNSS observations from smartphones have gained popularity in recent years due to the high precision achieved in various applications. While most studies have focused on signal quality evaluation, few have explored static and kinematic positioning. Furthermore, the majority of these studies have primarily concentrated on European and Asian countries. Therefore, we present the first study conducted in Northwest Mexico, which evaluates the performance of static and kinematic positioning using code and phase observations obtained from the Xiaomi Mi 8 smartphone. In addition, we assess the signal quality of ~ 100 available GNSS satellites. This study proposes an alternative method for analyzing the observed Carrier-to-Noise Density Ratio (C∕N 0) of GNSS observations in relation to theoretical reference values. The results reveal that the average C∕N 0 value of the GNSS satellites is approximately 18% lower than the reference values. Furthermore, the pseudorange observations indicate a significant multipath error, with magnitudes close to 200 cm for L1/E1 and less than 86 cm for L5/ E5a, highlighting the susceptibility of the smartphones GNSS antenna to this type of error. The static experiment demonstrates RMS positioning errors of 0.7 cm, 1.2 cm, and 4.2 cm for the E, N, and U components, respectively. Moreover, the kinematic experiment exhibits discrepancies of 1.4 cm due to the circular trajectory of the smartphone. Finally, the results suggest that dual-frequency smartphones offer promising positioning capabilities, presenting opportunities for engineering applications, including structural health monitoring, among others.
... Their research particularly highlighted the significance of carrier-to-noise ratio (CNR) values in determining the quality of smartphone measurements. References [8][9][10][11] concluded that the traditional elevation-weight method is not suitable for smartphones, while the CNR-weighted approach was deemed more suitable and effective. ...
The continuously improving performance of mass-market global navigation satellite system (GNSS) chipsets is enabling the prospect of high-precision GNSS positioning for smartphones. Nevertheless, a substantial portion of Android smartphones lack the capability to access raw carrier phase observations. Therefore, this paper introduces a precise code positioning (PCP) method, which utilizes Doppler-smoothed pseudo-range and inter-satellite single-difference methods. For the first time, the results of a quality investigation involving BDS-3 B1C/B2a/B1I, GPS L1/L5, and Galileo E1/E5a observed using smartphones are presented. The results indicated that Xiaomi 11 Lite (Mi11) exhibited a superior satellite data decoding performance compared to Huawei P40 (HP40), but it lagged behind HP40 in terms of satellite tracking. In the static open-sky scenario, the carrier-to-noise ratio (CNR) values were mostly above 25 dB-Hz. Additionally, for B1C/B1I/L1/E1, they were approximately 8 dB-Hz higher than those for B2a/L5/E5a. Second, various PCP models were developed to address ionospheric delay. These models include the IF-P models, which combine traditional dual-frequency IF pseudo-ranges with single-frequency ionosphere-corrected pseudo-ranges using precise ionospheric products, and IFUC models, which rely solely on single-frequency ionosphere-corrected pseudo-ranges. Finally, static and dynamic tests were conducted using datasets collected from various real-world scenarios. The static tests demonstrated that the PCP models could achieve sub-meter-level accuracy in the east (E) and north (N) directions, while achieving meter-level accuracy in the upward (U) direction. Numerically, the root mean square error (RMSE) improvement percentages were approximately 93.8%, 75%, and 82.8% for HP40 in the E, N, and U directions, respectively, in both open-sky and complex scenarios compared to single-point positioning (SPP). In the open-sky scenario, Mi11 showed an average increase of about 85.6%, 87%, and 16% in the E, N, and U directions, respectively, compared to SPP. In complex scenarios, Mi11 exhibited an average increase of roughly 68%, 75.9%, and 90% in the E, N, and U directions, respectively, compared to SPP. Dynamic tests showed that the PCP models only provided an improvement of approximately 10% in the horizontal plane or U direction compared to SPP. The triple-frequency IFUC (IFUC123) model outperforms others due to its lower noise and utilization of multi-frequency pseudo-ranges. The PCP models can enhance smartphone positioning accuracy.
... However, in 2016, thanks to the release of Android 7 (Nougat), users became able to collect raw GNSS data [1,2], making it possible for researchers to direct their efforts to the enhancements of smartphone positioning. Several research groups have studied and assessed the quality of observations and the positioning performance of different smartphone devices [3][4][5]. The low-cost receivers and antennas embedded in smartphones constitute the principal limitation to the positional accuracy of such devices, especially in signal-degraded environments where the presence of recurrent multipath phenomena strongly impacts the navigation solution. ...
... The most accurate positioning was obtained with the P30 Pro with a horizontal RMS of 3.24 m; Xiaomi Mi 8 and Xiaomi Mi 9 obtained RMS errors of 4.14 m and 4.90 m. Moreover, the positioning performance is significantly improved when using L5 and E5a frequency bands [6]. ...
Smartphone, as a device integrating communica-tion and navigation, plays an important role in urban pedestriantravel scenarios. In this study, we use the SPP (single-pointpositioning) method to analyze the short-time horizontalpositioning accuracy of the Huawei P30 and Oppo Reno5prosmartphones for three typical urban scenarios, focusing on theimpact of the ground plane on positioning accuracy, since theground plane is assumed to effectively reduce the multipatherrors. The experimental results show that applying the groundplane can effectively improve positioning accuracy. Therefore,using the ground plane is an effective way to improve dataquality when using smartphones to collect data.
... For instance, smartphones provide GNSSbased positioning services, but it is not their primary focus. These devices typically feature low-cost chipsets and antennas that are linearly polarized, resulting in higher signal loss compared to standard survey-grade GNSS receivers [22]. ...
This paper presents a comprehensive review of the Global Navigation Satellite System (GNSS) with Internet of Things (IoT) applications and their use cases with special emphasis on Machine learning (ML) and Deep Learning (DL) models. Various factors like the availability of a huge amount of GNSS data due to the increasing number of interconnected devices having low-cost data storage and low-power processing technologies - which is majorly due to the evolution of IoT - have accelerated the use of machine learning and deep learning based algorithms in the GNSS community. IoT and GNSS technology can track almost any item possible. Smart cities are being developed with the use of GNSS and IoT. This survey paper primarily reviews several machine learning and deep learning algorithms and solutions applied to various GNSS use cases that are especially helpful in providing accurate and seamless navigation solutions in urban areas. Multipath, signal outages with less satellite visibility, and lost communication links are major challenges that hinder the navigation process in crowded areas like cities and dense forests. The advantages and disadvantages of using machine learning techniques are also highlighted along with their potential applications with GNSS and IoT.
... Other studies reporting that poor data quality in smartphones is associated with signal-to-noise ratio are as follows. In this context, when the position accuracy performance of smartphones is tested, it is predicted that the C/N0 weighting model will be more appropriate than the elevation-dependent weighting model [9][10][11][12]. While positioning, navigation and timing applications with smartphones were made through single-frequency GNSS observations until 2018, Xiaomi produced and marketed Mi8 model smartphone that can collect dual-frequency GNSS raw observation data for the first time in May [13]. ...
In this study, positioning performance was evaluated by making single-frequency GNSS (Global Navigation Satellite System) observations under real-time conditions with a smartphone. In experiments, GNSS observations were recorded with the Xiaomi Redmi Note 8 Pro via the Geo++ RINEX Logger application. Measurements were made with the geodetic-grade CHC I80 GNSS receiver to evaluate the performance of the smartphone. In addition to the collected raw observation data set, solutions were realized with the Near-Real-Time Precise Point Positioning (N-RT-PPP) technique by using satellite orbit and clock correction products produced under real-time conditions from the CNES (National Centre for Space Studies) and MADOCA (Multi-GNSS Advanced Demonstration tool for Orbit and Clock Analysis) archives. When all the observations with the epoch difference are examined, it is observed that the root mean square error (RMSE) values of the GPS/GLONASS observations give better results than the only-GPS solutions. In addition, in the epoch differenced time series produced from the smartphone, an improvement between 92% and 98% was observed for the part below 1 cm horizontally and 2 cm vertically after the convergence.
