Introduction

Smartphones and tablets have become essential electronic devices in the daily lives of many people, offering a wide range of services and applications that simplify various everyday tasks and operations. Among these basic services, navigation applications stand out, relying on GNSS positioning technology for accurate location information. In the past, smartphones faced technical limitations in positioning, leading to reduced accuracy and performance due to limited access to pseudorange, Doppler, or carrier-phase observables (Robustelli et al. 2019; Chen et al. 2019; Geng and Li 2019). Since the release of Android operating system version 7.0 in 2016, raw GNSS measurements have become accessible for compatible devices, providing an opportunity for the GNSS research community to assess and enhance timing, positioning, and navigation applications using smartphones (Li and Geng 2019; Paziewski 2020; Dabove et al. 2020; Liu et al. 2021). The built-in chipsets have also advanced significantly, allowing the evaluation of positioning capabilities in various modalities, such as RTK (Real-time Kinematic) (Odolinski and Teunissen 2019; Gao et al. 2021; Yong et al. 2021, 2022), single and dual-frequency PPP (Precise Point Positioning) (Liu et al. 2019; Wu et al. 2019; Wen et al. 2020; Wang et al. 2021; Yi et al. 2021, 2022; Shinghal and Bisnath 2021; Li et al. 2021) and relative positioning (Paziewski et al. 2019, 2021).

In recent years, built-in smartphone chipsets have undergone significant advancements. Between 2016 and 2018, many chipsets were multi-constellation but single-frequency, requiring the use of models like Klobuchars to estimate ionospheric error, which resulted in performance limitations. A breakthrough occurred in 2018 with the release of the Xiaomi Mi8, the first dual-frequency GNSS smartphone featuring the Broadcom BCM47755 chipset. This cutting-edge chip can track GPS (L1, C/A, L5), GLONASS (G1), BeiDou (B1), QZSS (L1, L2), and Galileo (E1, E5a) (Guo et al. 2020). 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 and Robustelli 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. In the literature, several studies focusing on the performance of smartphones in differential or relative positioning approaches are available. For example, Bahadur (2022) conducted a kinematic experiment using three smartphones in a real-time standalone positioning scenario. By utilizing single-frequency code observations, the study revealed an average improvement of 11% in the positioning accuracy of the smartphones. Similarly, Aggrey et al. (2020) explored the potential of enhancing positioning accuracy through raw GNSS data obtained from dual-frequency chipsets. The results demonstrated notable improvements in both static and kinematic positioning over a 6-hour period, especially during the initial minutes. Furthermore, Bakuła et al. (2020, 2022), evaluated the performance of pseudorange measurements in smartphone positioning, comparing the L1 and L5 frequencies using Differential Global Position System (DGPS) and Differential Global Navigation Satellite System (DGNSS). The findings indicated that using the L5 code, a centimeter-level accuracy in 3D positioning can be achieved, with the capability to track 16 to 26 satellites. Additionally, Fortunato et al. (2019) conducted a study utilizing a Xiaomi Mi8 to evaluate the devices positioning capabilities for geophysical applications, including real-time observations and ionosphere monitoring. The research revealed that in a static scenario, the noise level for the horizontal and vertical position components was in the order of millimeters and centimeters, respectively. The experimental results demonstrated that the Standalone Positioning (SPP) approach could obtain similar results to the Differential Global Navigation Satellite System (DGNSS) solution, achieving positioning accuracy typically in the order of meters. Another study by Pepe et al. (2021) established that DGNSS generally outperforms SPP for altimetric positioning. Similarly, Realini et al. (2017) examined the accuracy and feasibility of relative positioning using a single-frequency smartphone in relation to a base station. The research revealed that decimeter-level accuracy can be achieved with raw GNSS phase observations without phase ambiguity resolution. More recent studies have focused on analyzing the positioning accuracy of low-cost smartphones in different environments, such as urban areas (Specht et al. 2020; Uradziński and Bakuła 2020) and forests (Tomaštík et al. 2021; Tomaštík and Varga 2021). These experiments concluded that GNSS adverse effects, particularly multipath, significantly degrade the quality and accuracy of smartphone positioning (Zhu et al. 2020). However, when compared to single-frequency smartphones, multi-constellation dual-frequency devices offered more reliable and accurate position measurements (Wanninger and Heßelbarth 2020; Zangenehnejad and Gao 2021; Yong et al. 2022). Moreover, it was assessed the GNSS performance capabilities using low-end hardware in Android smartphones, since most studies are based on high-end hardware which implies better internal components. The results demonstrate that the observable data quality of these smartphones maintain low performance compared to flagships smartphones (Bramanto et al. 2023). Furthermore, other research work conducted by Li et al. (2023) evaluate the high-precision velocity using GNSS measurements retrieved by Android smartphones using the App Geo + + RINEX Logger. The authors stated that the Doppler-estimated receiver clock drifts in conjunction with frequent jumps leads to anomalous clock variations. Finally, Cheng et al. (2023) proposed an alternative approach for real-time kinematic precise point positioning (PPP-RTK) using GNSS data recorded by Android smartphones. The results indicated that the post-processing time needed to achieve centimeter-level precisions in stand-alone mode is ~ 1 min, which provide a relatively rapid approach for high-precision GNSS positioning using smartphones.

In conclusion, there is a growing interest in utilizing low-cost smartphones with integrated GNSS chipsets for positioning applications, and numerous studies are being conducted to evaluate their positioning accuracy. It is essential to assess the quality and performance of these devices in various regions worldwide, including mid-latitudes, as highlighted in studies by Zhang et al. (2018) and Zeng et al. (2022). Such evaluations will be crucial for advancing these smartphones into professional-level surveying devices in the near future.

Considering all the aspects discussed above, the aim of this study is to analyze the signal quality for each satellite observed using a newly proposed method that incorporates theoretical observations of the Carrier-to-Noise Density Ratio (\(C/{N}_{0}\)). We evaluated the performance of kinematic and static positioning using raw observations a Xiaomi Mi 8 smartphone equipped with a dual-frequency Broadcom BCM47755 chipset in Northwest Mexico. Quality analysis involves examining GNSS signals over a 24-hours measurement period, focusing on the visibility of individual satellite, constellations, and frequencies. Furthermore, the performance of kinematic positioning is evaluated considering tracking a circular trajectory. The paper is organized as follows: Section “Experimental setup and data acquisition” describes the experimental setup and data acquisition, encompassing static and kinematic measurements. Section “Static GNSS measurements” provides a description of the different parameters utilized to assess GNSS signal quality. Section “Kinematic GNSS measurements” presents the analysis and experimental findings of this study. In Sections. “GNSS signal quality assessment” and “Carrier-to-noise density ratio (C/N0)”, the results and conclusions are discussed.

Experimental setup and data acquisition

GNSS observations from a Xiaomi Mi 8 smartphone were collected and evaluated under three different settings: static positioning for signal quality assessment over a long period, dynamic positioning performance evaluation, and three-dimensional positioning in a static test. Detailed methodologies for each setting will be presented in subsequent sections.

The Xiaomi Mi 8 smartphone is equipped with the Broadcom BCM47755 dual GNSS chip, capable of receiving simultaneous signals as listed in Table 1. In the Table, the observables correspond to the RINEX 3.04 format recorded by the smartphone using the Geo + + Rinex Logger APP. Unlike other smartphone models, the Xiaomi Mi 8 possesses the advantage of acquiring both code and phase observables from all GNSS constellations, with the duty cycle disabled.

Table 1 GNSS signals that the Xiaomi Mi 8 smartphone can track. C represents the code observation, L is the phase observable, D is the Doppler measurement, S is the noise measurement, G denotes the GPS constellation, R stands for GLONASS, E for Galileo, C for BeiDou, and J for QZSS
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Static GNSS measurements

The Xiaomi Mi 8 smartphone was vertically positioned with support on a forced centering monument on the roof of a residential house, as shown in Fig. 1. The concrete monuments location provided good signal coverage and optimal measurement conditions, with minimal possibilities of generating multipath due to the absence of obstructions. Any multipath values obtained were predominantly attributed to the smartphones embedded antenna characteristics. In the first experiment, the objective was solely to evaluate the acquired GNSS signal, eliminating the need for conventional centering. Additionally, maintaining a static position ensured that changes in the antennas position would not affect the behavior of the quality parameters. \(C/{N}_{0}\) and multipath were chosen as quality parameters, as they are commonly used to evaluate GNSS signals from smartphones (Robustelli et al. 2019; Paziewski et al. 2019, 2021; Guo et al. 2020; Wanninger and Heßelbarth 2020).

Fig. 1
figure 1

Xiaomi Mi 8 smartphone on the concrete monument during GNSS measurements. A ground plane was positioned underneath the smartphone to mitigate multipath effects

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On 15th January 2023, 24-hour continuous raw GNSS observations were collected from the Xiaomi Mi 8 smartphone at a sampling rate of 1 Hz, with an elevation mask set at 0 degrees. The low elevation mask ensured tracking of all GNSS satellites, enabling correlation with the quality parameters evaluated in this study. For the static experiments measurement period, Bluetooth and Wi-Fi functions were disabled, and the Geo + + Rinex Logger app was used to store the measurements in RINEX 3.04 format. The “Force Full GNSS Measurements” native function was enabled to remove smartphone battery power-saving (duty cycle) during measurements. The Anubis software was used to extract the number of GNSS satellites per measurement epoch, as well as the values of the carrier/noise density ratio (\(C/{N}_{0}\)) and multipath for satellites with dual frequencies (Vaclavovic and Dousa 2015).

Figure 2 shows the skyplot and the number of GNSS satellites observed during the experiment, as well as the availability of bands for each satellite. Initially, difficulties were encountered in maintaining tracking of the Galileo and BeiDou satellites over extended measurement periods. However, this issue was resolved by resetting the parameters of the dual GNSS chip in the Xiaomi Mi 8 smartphone, resulting in significant improvements in the measurements of Galileo and BeiDou satellites, with only occasional and short-term track losses. Subsequently, it was identified that a regulation by the Federal Communications Commission (FCC) in the United States of America (USA) was causing the tracking issue with the Galileo and BeiDou constellations. The close proximity between the smartphone location and the USA led to potential interference with the Galileo and BeiDou signals. The FCC regulation stipulated that any non-federal receiver in the USA utilizing foreign GNSS signals must be licensed. Although the ban was later lifted by the FCC, many devices were not updated to track these constellations (FFC 2023). As a result, some nations currently have limited Galileo and BeiDou coverage on dual GNSS chips, depending on the smartphone manufacturer.

Fig. 2
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Skyplot and number of available bands of the GNSS satellite tracked during the experiment in Northwest Mexico

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In Northwestern Mexico, a Xiaomi Mi 8 smartphone was able to acquire GNSS observations from 100 satellites, which consisted of 31 GPS satellites (12 dual-frequency L1, L5 and 19 single-frequency), 24 single-frequency GLONASS satellites (G1), 24 dual-frequency Galileo satellites (E1, E5a), and 21 single-frequency BeiDou satellites (B1). Among the BeiDou satellites, 3 were from the BDS-2 block, and 18 were from the BDS-3 block, with only the BeiDou Medium Earth Orbit (MEO) satellites being visible. The results obtained from the Xiaomi Mi 8 smartphone were consistent with the satellites tracked by the GNSS antenna HITAT35101CR HITS, a geodetic-order antenna from Hi Target located approximately 8 km away from the experimental area (See Fig. 2).

The number of satellites tracked for each constellation and frequency during all measurement times is shown in Fig. 3.

Fig. 3
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Number of satellites tracked by GNSS constellation and frequency throughout the continuous 24-hour measurement period

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Kinematic GNSS measurements

The objective of this experiment was to assess the kinematic performance of the Xiaomi Mi 8 smartphone using multi-GNSS observations and considering different combinations (G, G + E, G + R + E + C, G + R + C). To achieve this, a circular motion device was designed, consisting of a servomotor capable of rotating at a speed of 0.44 rad/s and a 45 cm long metal bar centered over the axis of the servomotor. The Xiaomi Mi 8 smartphone was vertically positioned at a distance of 19 cm from the axis, as depicted in Fig. 4. During the kinematic experiment, observations were collected at 1 Hz with an elevation mask set at 10 degrees, covering signals from the GPS, GLONASS, Galileo, and BeiDou constellations. Additionally, given the known radius of the circular path, it is possible to determine, in each epoch, the distance between the center of rotation and the phase center of the smartphone. The measurement period extended approximately 5 h. Figure 5 illustrates the number of available satellites per constellation during the kinematic experiment.

The kinematic experiment was carried out under the same environmental conditions as the previous experiment. Post-processing was performed using the relative kinematic method with the RTKLIB software, utilizing the reference station equipped with the HITAT35101CR HITS antenna.

Fig. 4
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The Xiaomi Mi 8 smartphone mounted on the rotary servo motor

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Fig. 5
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Number of satellites available per GNSS constellation during the kinematic experiment

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GNSS signal quality assessment

This section evaluates the GNSS signal quality of the Xiaomi Mi 8 smartphone using two key indicators: The Carrier-to-Noise Density Ratio (\(C/{N}_{0}\)) and pseudorange multipath. Additionally, a novel approach is introduced to evaluate signal quality based on theoretical \(C/{N}_{0}\) values for each frequency of the four global GNSS constellations.

Carrier-to-noise density ratio (\(\varvec{C}/{\varvec{N}}_{0}\))

The strength of the GNSS signal is commonly measured using the Carrier-to-Noise Density Ratio (\(C/{N}_{0}\)) or Signal-to-Noise Ratio (SNR), which is indicative of the GNSS receivers’ performance and the quality of carrier-phase and pseudorange observations. Geodetic antennas and receiver manufacturers analyze \(C/{N}_{0}\) observations to improve their products technical specifications and signal quality acquisition. According to Langley (1997), the noise density (\({N}_{0}\)) is affected by thermal noise sources, such as antenna and receiver temperature, which involve the motion of electrons within the receiver hardware. The noise temperature (\(T\)) for a typical receiver under normal conditions is approximately 290 K, leading to a noise density of -204 dBW/Hz when considering Boltzmann’s constant \(k\) = \(1.38\times {10}^{-23} J/K\). On the other hand, the received carrier power (\(C\)) is influenced by various factors, including satellite transmitter power output, changes in space loss, receiver antenna gain pattern, and signal losses between the antenna cable and receiver. For example, the nominal carrier power received for GPS L1 C/A is around − 158.5 dBW, representing the minimum signal level referenced to a 0 dB gain for an isotropic circularly polarized antenna. Additionally, the maximum received power (\({C}_{MAX}\)) of a GNSS signal is crucial for calculating possible interference from the actual signal. Determining the maximum received power for a GNSS signal involves considering losses and gains of the signal propagation medium, incorporating spatial and physical factors such as: RF transmit power, equivalent transmit antenna gain towards the receiver antenna, gain of maximum received power with respect to the reference antenna, free space propagation loss at a given off-nadir angle, minimum possible atmospheric loss maximizing received power, and polarization loss. Readers are referred to Betz (2010) for specific details on the aforementioned parameters description. Furthermore, both minimum and maximum GNSS receiver power values increase depending on the satellite elevation angle (e) relative to the receiver antenna. For example, the maximum GPS received power for each frequency varies by approximately ~ 2.3 dBW over elevation angles from 0 to 90 degrees (Betz 2016).

The interface design information for each satellite navigation system (GPS, GLONASS, Galileo, and BeiDou) is formally described in its respective Interface Control Document (ICD). These ICDs contain essential details, constants, parameters, definitions, and procedures concerning the system interface. Each government agency responsible for the respective satellite navigation system maintains and updates its ICD with each new issue. In this study, the information from each ICD was used to calculate the mean of the theoretical minimum power received in units of dBW for each satellite navigation system, frequency band, and satellite block. Additionally, the noise density for a typical in-ground receiver was utilized to convert each value to dB-Hz. The results are summarized in Table 2.

Table 2 Theoretical minimum and maximum received power levels reported for the different GNSS constellation
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The \(C/{N}_{0}\) values of the GNSS observations from the Xiaomi Mi 8 were analyzed considering two factors: (1) the elevation angle of the GNSS satellites, and (2) the theoretical \(C/{N}_{0}\) values as proposed in Table 2. Figure 6 shows the relation between the elevation angle of GNSS satellites and their corresponding \(C/{N}_{0}\) values for each frequency. The analysis revealed generally low \(C/{N}_{0}\) values. The latter should include a degradation in the signal quality in a range of ~ 10 and ~ 15 dB-Hz lower than those recorded by geodetic-grade receivers (Li et al. 2021; Everett et al. 2022; Bramanto et al. 2023). Among the satellites, R14, R15, and R20 displayed the highest \(C/{N}_{0}\) values concerning the elevation angle, while GPS, Galileo, and BeiDou experienced the lowest values. It is noteworthy that the highest \(C/{N}_{0}\) values for each GNSS constellation did not necessarily occur at the highest elevation angles. Some satellites demonstrated similar \(C/{N}_{0}\) magnitudes within the range of 20° to 80° elevation angle (e.g., satellite E25). Consequently, the irregularity of the \(C/{N}_{0}\) values in the Xiaomi Mi 8 GNSS observations is attributed to the quality of the antenna (possibly irregular gain) (Pesyna et al. 2014), rather than being influenced by the elevation angle of the satellites (as observed in geodetic-grade antennas) or the antennas environmental conditions.

Fig. 6
figure 6

Relation between the elevation angle of the GNSS satellites and the corresponding values for each frequency and constellation \(C/{N}_{0}\) values for each frequency and constellation

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Figure 7 shows the \(C/{N}_{0}\) values as a percentage (%) of the maximum theoretical values obtained from Table 2 for all registered GNSS satellites and frequencies. In general, for both GPS and Galileo, the L1/E1 signal scores were higher compared to L5/E5a. Among the constellations, GLONASS satellites achieved scores close to 100% for some satellites (R02, R11, R12, R24), making it the only constellation to reach this percentage. On the other hand, BeiDou had the lowest percentages in one of its satellites (C26, C32, C37). The mean score for each constellation and frequency is presented in Table 3, with the GLONASS (G1) constellation obtaining the highest score of 88.23%, while Galileo (E5a) displayed the lowest score at 66.40%. These results may be attributed to the design of the GLONASS orbit and the FCC restrictions on the Galileo and BeiDou signals.

Fig. 7
figure 7

Percentage of observed \(C/{N}_{0}\) values with respect to the theoretical maximum \(C/{N}_{0}\) for all satellites and GNSS frequencies recorded by the Xiaomi Mi 8 during the continuous 24-hour measurements. The blue and orange lines represent 100% of the \(C/{N}_{0}\) score for single and double frequency, respectively

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Table 3 Mean \(C/{N}_{0}\) score in percentage (%) for each constellation and frequency
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Pseudorange multipath

The linearly polarized GNSS antenna of the Xiaomi Mi 8 smartphone is susceptible to multipath (Robustelli et al. 2019), which is a significant source of error affecting smartphone positioning accuracy. Multipath is an important indicator of GNSS signal quality (Yun et al. 2022). For that reason, in this study the impact of multipath was evaluated on the GPS and Galileo constellations, that is, the two constellations that emit dual-frequency signals among the registered by the Xiaomi Mi 8. It is noteworthy that this analysis is one of the few studies to examine multipath effects in the pseudorange measurements of a smartphone. Figure 8 illustrates the relation between the elevation angle and multipath values for four GPS and Galileo satellites (the highest elevation). This study found that the L1 and E1 frequencies are affected approximately four and two times more than the L5 and E5a frequencies, respectively. This disparity is attributed to the high chipping rate of L5/E5a (Leclère et al. 2018), making them less susceptible to multipath effects (Circiu et al. 2017).

Fig. 8
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Relation between multipath and the satellite elevation angle for the dual frequencies of the GPS and Galileo constellations

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Figure 8 shows that the elevation angle has minimal impact on the multipath values, which are predominantly influenced by the poor quality of the smartphones GNSS antenna. Conversely, Fig. 9 displays the mean pseudorange multipath values for GPS and Galileo satellites with dual frequency. The mean multipath values for GPS (L1) satellites were consistently around 200 cm, while GPS (L5) exhibited lower mean values, approximately 80 cm. For Galileo satellites (E1/E5a), the mean multipath values presented a slight difference and were lower than those of GPS. These findings further support the hypothesis that GNSS antenna issues primarily cause multipath errors in smartphones.

Fig. 9
figure 9

Average multipath from both dual frequency GPS and Galileo satellites observed by the Xiaomi Mi 8

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Table 4 summarizes the mean multipath error for both the GPS and Galileo constellations. The GPS satellites operating at L1 and L5 frequencies experienced multipath errors of approximately 2.187 m and 0.636 m, respectively. For the Galileo satellites at E1 and E5a frequencies, the multipath errors were approximately 1.683 m and 0.853 m, respectively. It is important to highlight that the noise level of the GNSS pseudorange observation of the smartphone is high (up to tens of meters) (Paziewski 2020) and is incorporated into the pseudorange multipath. Furthermore, the multipath values for the E1 and E5 signals of the Galileo and L5 GPS satellites presented lower values than those of GPS L1, such as those reported by Circiu et al. (2017). Overall, the mean multipath values for each GPS and Galileo satellite remained below 3 m. It is noteworthy that these values are lower than those reported in other studies (Robustelli et al. 2019).

Table 4 Mean multipath for the GPS and Galileo constellations
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Kinematic experiment results

Figure 10 illustrates the kinematic solutions of the circular trajectory considering various GNSS combinations, including G, G + E, G + R + E + C, and G + R + C. To assess the kinematic performance of the Xiaomi Mi 8, the root mean square (RMS) was utilized as an indicator of precision. The RMS was computed based on the difference between the reference distance (19 cm) and the distances calculated using the East (E) and North (N) coordinates. The smoothest trajectory was achieved with the G + E combination, which exhibited an RMS of 1.4 cm. Comparatively, the other combinations yielded similar RMS values, with differences of less than 0.9 cm.

Fig. 10
figure 10

Circular trajectory tracking of kinematic results obtained from various GNSS combinations. The blue dots represent the kinematic solutions of the Xiaomi Mi 8, and the black circle represent the reference circular movement

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By leveraging multi-GNSS and multi-frequency observations from a Xiaomi Mi 8 smartphone, kinematic measurements can attain precisions of less than 2 cm. The experiment was conducted in a location with unobstructed sky visibility, which played a significant role in achieving accurate results. Additionally, establishing a baseline of less than 8 km further contributed to achieving horizontal component accuracies comparable to those of low-cost GNSS receivers. However, it should be noted that due to the lack of a vertical reference in the ensemble, the evaluation was focused solely on the horizontal component. Nonetheless, the study findings highlight the potential of using a Xiaomi Mi 8 smartphone with multi-GNSS and multi-frequency capabilities for precise kinematic measurements in practical engineering applications, such as structural monitoring.

GNSS Positioning performance of the Xiaomi Mi 8

The experiment was conducted on the roof of the Faculty of Earth and Space Sciences at the Autonomous University of Sinaloa in Mexico. To assess the precise three-dimensional positioning performance of the Xiaomi Mi 8, a baseline of approximately 10 m was set up. The reference station comprised a low-cost Mosaic X5 receiver and a LEIAS AS10 geodetic antenna. The Xiaomi Mi 8 was configured as a rover and set on a vertically forced centering monument with the help of a fixed support on a tribrach. Using geodetic order equipment and the relative static method, precise coordinates for the forced centering monument were determined. In contrast, processing in PPP mode was negligible since the precision achieved by the latter using smartphone observations is on the order of meters (Robustelli et al. 2019). Also, an ENU (East-North-Up) topocentric coordinate system with origin at the reference station was established. The Xiaomi Mi 8 antenna was oriented to the North, as illustrated in Fig. 11.

To achieve precise centering at ~ 1 cm accuracy, the Antenna Reference Point (ARP) located on the top of the Xiaomi Mi 8 was taken into consideration (refer to Fig. 11). The distance between the ARP and CRP (Centering Reference Point) was measured, and using the phase center location of the antenna reported by Netthonglang et al. (2019), displacements of the general phase center were estimated and presented in Table 5. As a result, the coordinates obtained by the Xiaomi Mi 8 were referenced to the CRP, which is the same reference point as the known coordinates of the forced centering monument. This was done to evaluate the positioning performance of the Xiaomi Mi 8 by comparing its reference coordinates with those estimated by the smartphone.

Fig. 11
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Xiaomi Mi 8 vertically centered and leveled on a force-centered monument. Setting of ENU coordinates and antenna orientation

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Table 5 Offset of Xiaomi Mi 8 antenna phase center with respect to the CRP
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During the experiment, a total of 11 GPS satellites, 10 GLONASS satellites, 5 Galileo satellites, and 6 BeiDou satellites were visible. Observations were collected at a sampling frequency of 1 s using the Geo + + RINEX Logger app, and the data was gathered for a duration of one hour. To ensure accuracy, Wi-Fi and Bluetooth were disabled during data collection. The GNSS observations from the low-cost reference receiver and the Xiaomi Mi 8 were saved in RINEX 3.04 format to be later processed in relative static mode using the RTKLIB software (Takasu and Yasuda 2009; Everett et al. 2022).

Processing strategies

The Xiaomi Mi 8 GNSS observations were processed using the open-source software RTKLIB demo5 b34g (Takasu and Yasuda 2009; Everett et al. 2022) with processing strategies outlined in Table 6. To evaluate multi-GNSS positioning performance, 8 GNSS combinations were established: G, E, G + R + E + C, G + E, G + R + E, R + E, R + E + C and E + C. Previous studies have evaluated relative positioning by means of geodetic-grade GNSS receivers and smartphones (Geng and Li 2019; Uradziński and Bakuła 2020) and smartphones only (Paziewski et al. 2021). These studies demonstrated centimeter-level differences between the estimated solutions and the reference values. In this study, we established a short baseline comprising a Xiaomi Mi 8 smartphone and a low-cost GNSS receiver with a geodetic-grade antenna.

Table 6 Processing strategies for the RTKLIB software demo5b34g
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Static experiment results

Figure 12 illustrates the relative static positioning errors of the smartphone for each GNSS combination. Significant discrepancies were observed in specific combinations, including G, E, E + C and R + E + C, which exhibited the most pronounced discrepancies in both the horizontal and vertical components. In addition, the positioning performance of the Xiaomi Mi 8 was evaluated using precision indicators such as the mean (\(\stackrel{-}{x}\)), standard deviation (\(\sigma\)) and root mean square error (RMSE). Table 7 presents these precision indicators for each GNSS combination. Among the combinations tested, G + R + E + C exhibited the best precision, with RMS values of 0.7 cm, 1.2 cm, and 4.2 cm for the East, North, and Up components, respectively. Notably, combinations involving both GPS and Galileo showed similar accuracies, indicating that achieving high precision and fixing ambiguities in three-dimensional positioning with a smartphone requires using both GPS and Galileo constellations simultaneously. Conversely, the combination R + E resulted in the lowest precision, with RMS values of 27.6 cm, 14.9 cm, and 45.1 cm for the East, North, and Up components, respectively. Furthermore, the performance of using only GPS or Galileo constellations was poor in achieving fixed ambiguity percentages. Therefore, it is not recommended to rely solely on isolated GPS or Galileo constellations for relative positioning using a Xiaomi Mi 8 smartphone.

Fig. 12
figure 12

Relative static positioning errors for GNSS combinations. a horizontal error; b vertical error

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Table 7 Statistical summary of static relative positioning errors
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GPS and Galileo are the most desirable constellations to obtain reliable static positioning accuracy with GNSS measurements from a Xiaomi Mi 8 smartphone due to the availability of dual-frequency transmissions (Magalhães et al. 2021; Purfürst 2022; Yun et al. 2022), such as those achieved in this study. In contrast, GLONASS and BeiDou are less recommended due to their limited capability to track dual frequencies that represents a lower precision as reported in Table 7 where these constellations only in combination with other constellations (GPS or Galileo) achieved fixed ambiguity solutions.

Conclusions and future work

This study evaluates the GNSS signal quality recorded from a Xiaomi Mi 8 smartphone and proposes a signal evaluation strategy based on theoretical \(C/{N}_{0}\) values. The performance of kinematic and static positioning is also analyzed. The results reveal that the smartphones GNSS observations have lower quality compared to theoretical reference values, particularly for GPS, Galileo, and BeiDou, which are not affected by the elevation angle. The \(C/{N}_{0}\) values of GLONASS satellites are the only ones that present a high correlation with the satellite elevation angle. The average \(C/{N}_{0}\) score for GPS (L1/L5), GLONASS (G1), Galileo (E1/E5a), and BeiDou (B1) is reported as 81.06/77.48%, 88.23%, 76.36/66.10%, and 75.04%, respectively, with an overall average 18% below theoretical reference values, mainly attributed to the bad quality smartphones GNSS antenna. However, this does not impede precise positioning. Moreover, the study found that the L1 and E1 frequencies were particularly susceptible to multipath errors, with mean values of 218.7 cm and 168.3 cm, respectively. In contrast, the L5 and E5a frequencies exhibited lower multipath errors, with values less than 86 cm. The main cause of the multipath generation on the Xiaomi Mi 8 smartphone is attributed to the linear polarization of the GNSS antenna. This study did not find a direct correlation between the occurrence of multipath errors and the elevation angle of the satellites.

Using smartphone multi-frequency and multi-constellation observations enables sub-centimeter and centimeter-level accuracies in static and kinematic positioning, respectively. In the kinematic experiment, the Xiaomi Mi 8 achieves an RMS error of 1.4 cm in the horizontal component while following a circular trajectory. For the G + R + E + C combination, static positioning results show RMS errors of 0.7 cm, 1.2 cm, and 4.2 cm in the East, North, and Up components, respectively.

The achieved results demonstrate the Xiaomi Mi 8 potential as a valuable tool in engineering sciences. Therefore, our future work will focus on utilizing the latest GNSS chipsets capable of tracking dual-frequency signals for structural health monitoring in bridge applications.