Smartphone GNSS Research Articles

Improving smartphone GNSS positioning in challenging urban environments using GA-BPNN

Improving smartphone GNSS positioning in challenging urban environments using GA-BPNN

Comprehensive Analysis of Xiaomi Mi 8 GNSS Antenna Performance.

The interest in precise point positioning techniques using smartphones increased with the launch of the world's first dual-frequency L1/L5 GNSS smartphone, Xiaomi Mi 8. The smartphone GNSS antenna is low-cost, sensitive to multipath, and limited by physical space and design. The main purpose of this work is to determine the mechanical location and antenna performance in terms of radiation pattern in an anechoic chamber using a Vector Network Analyzer (VNA) and robotic positioning platform by varying the elevation and azimuth angles between the transmitter and smartphone GNSS antennas; the power received and satellite visibility are developed in an outdoor scenario. The results show a Planar Inverted-F Antenna with an omnidirectional radiation pattern without gain. The L1/E1/B1 and L5/E5a/B2a GNSS antennas are physically located at the top face of the screen, with dimensions of 48 × 17 mm and 60 × 13 mm, respectively. With the screen with line-of-sight toward the sky, L5 satellites have a better signal-noise ratio (SNR), unlike the back side, which loses 99% of the data in the PPP solution. Under multipath scenarios, the L1 GNSS smartphone antenna works with 25% less power than the GPS user segment recommendation, showing high sensitivity to track weak signals.

Smartphone-based Vision/MEMS-IMU/GNSS tightly coupled seamless positioning using factor graph optimization

With the diversification of functionalities and enhancement of performance in smartphones, low-cost GNSS chips, Micro-Electro-Mechanical System (MEMS) inertial measurement units (IMU), and cameras have become essential hardware components. Moreover, the computational power provided by smartphones for data processing has also become more robust. The fusion of multiple sensors in smartphones for positioning has emerged as a trend. In order to further enhance the positioning performance of smartphones in complex environments, this paper proposes a smartphone-based Vision/MEMS-IMU/GNSS tightly coupled integration for indoor-outdoor seamless positioning. Factor graph optimization is employed to combine observations from monocular vision, MEMS-IMU, and GNSS. To solve the unstable timing of current smartphone sensors, we present a GNSS-to-VIO time synchronization method. Furthermore, an adaptive positioning mechanism is devised based on the presence or absence of GNSS signals, allowing indoor and outdoor environment to switch. The proposed method is validated by integrating smartphone VIO with both smartphone built-in GNSS and high-performance geodetic GNSS receivers. The test results of the complex outdoor environments demonstrate that the proposed method significantly enhances GNSS positioning performance. The positioning accuracy of the proposed method using geodetic GNSS receiver is superior to that using smartphone GNSS. In outdoor environments, our method enables positioning accuracy of smartphone to improve 29.4, 53.0 and 69.9 % in three directions compared to single point positioning. Indoor and outdoor tests verified that the proposed method can provide continuous and reliable positioning in alternating interrupted and restored GNSS signals.

A Cloud-native Approach for Processing of Crowdsourced GNSS Observations and Machine Learning at Scale: A Case Study from the CAMALIOT Project

The era of modern smartphones, running on Android version 7.0 and higher, facilitates nowadays acquisition of raw dual-frequency multi-constellation GNSS observations. This paves the way for GNSS community data to be potentially exploited for precise positioning, GNSS reflectometry or geoscience applications at large. The continuously expanding global GNSS infrastructure along with the enormous volume of prospective GNSS community data bring, however, major challenges related to data acquisition, its storage, and subsequent processing for deriving various parameters of interest. In addition, such large datasets cannot be managed manually anymore, leading thus to the need for fully automated and sophisticated data processing pipelines. Application of Machine Learning Technology for GNSS IoT data fusion (CAMALIOT) was an ESA NAVISP Element 1 project (NAVISP-EL1-038.2) with activities aiming to address the aforementioned points related to GNSS community data and their exploitation for scientific applications with the use of Machine Learning (ML). This contribution provides an overview of the CAMALIOT project with information on the designed and implemented cloud-native software for GNSS processing and ML at scale, developed Android application for retrieving GNSS observations from the modern generation of smartphones through dedicated crowdsourcing campaigns, related data ingestion and processing, and GNSS analysis concerning both conventional and smartphone GNSS observations. With the use of the developed GNSS engine employing an Extended Kalman Filter, example processing results related to the Zenith Total Delay (ZTD) and Slant Total Electron Content (STEC) are provided based on the analysis of observations collected with geodetic-grade GNSS receivers and from local measurement sessions involving Xiaomi Mi 8 that collected GNSS observations using the developed Android application. For smartphone observations, ZTD is derived in a differential manner based on a single-frequency double-difference approach employing GPS and Galileo observations, whereas satellite-specific STEC time series are obtained through carrier-to-code leveling based on the geometry-free linear combination of observations from both GPS and Galileo constellations. Although the ZTD and STEC time series from smartphones were derived on a demonstration basis, a rather good level of consistency of such estimates with respect to the reference time series was found. For the considered periods, the RMS of differences between the derived smartphone-based time series of differential zenith wet delay and reference values were below 3.1 mm. In terms of satellite-specific STEC time series expressed with respect to the reference STEC time series, RMS of the offset-reduced differences below 1.2 TECU was found. Smartphone-based observations require special attention including additional processing steps and a dedicated parameterization in order to be able to acquire reliable atmospheric estimates. Although with lower measurement quality compared to traditional sources of GNSS data, an augmentation of ground-based networks of fixed high-end GNSS receivers with GNSS-capable smartphones would however, form an interesting source of complementary information for various studies relying on GNSS observations.

Velocity Estimation Using Time-Differenced Carrier Phase and Doppler Shift with Different Grades of Devices: From Smartphones to Professional Receivers

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.

An improved Kloubuchar ionospheric correction model for single frequency GNSS receivers

Abstract The enhancement of positional accuracy of single frequency ionospheric correction models is an urgent need for low-cost and smartphone GNSS users. The available single frequency ionospheric correction models such as Klobuchar, NeQuick-G, NTCM, and Klob-BDS are providing ionospheric corrections for multi GNSS systems such as GPS, Galileo, BDS, and NAVIC systems. Otherwise, Global Ionospheric Map (GIM) Total Electron Content (TEC) corrections are also available to the GNSS users. In this letter, an improved Klobuchar ionospheric model is implemented. The slant TEC of dual Frequency GPS TEC observations is considered a reference. The Klobuchar model slant TEC observations are improved by taking the grid-based residual TEC corrections with the Adjusted Spherical Harmonic Function model. The Single frequency users can improve the ionospheric delay estimation using Klobuchar model and grid-based TEC residual corrections. The improved ionospheric correction model is tested under the biggest geomagnetic storm conditions during 24th Solar cycle that occurred in March 2015 over India. The proposed hybrid slant ionospheric TEC algorithms are evaluated with individual single frequency ionospheric models (Klobuchar, and GPS TEC) under adverse space weather conditions.

An Improved Ambiguity Resolution Algorithm for Smartphone RTK Positioning.

Ambiguity resolution based on smartphone GNSS measurements can enable various potential applications that currently remain difficult due to ambiguity biases, especially under kinematic conditions. This study proposes an improved ambiguity resolution algorithm, which uses the search-and-shrink procedure coupled with the methods of the multi-epoch double-differenced residual test and the ambiguity majority tests for candidate vectors and ambiguities. By performing a static experiment with Xiaomi Mi 8, the AR efficiency of the proposed method is evaluated. Furthermore, a kinematic test with Google Pixel 5 verifies the effectiveness of the proposed method with improved positioning performance. In conclusion, centimeter-level smartphone positioning accuracy is achieved in both experiments, which is greatly improved compared with the float and traditional AR solutions.

Improving Smartphone GNSS Positioning Accuracy Using Inequality Constraints

To improve smartphone GNSS positioning performance using extra inequality information, an inequality constraint method was introduced and verified in this study. Firstly, the positioning model was reviewed and three constraint applications were derived from it, namely, vertical velocity, direction, and distance constraints. Secondly, we introduced an estimator based on the density function truncation method to solve the inequality constraint problem. Finally, the performance of the method was investigated using datasets from three smartphones, including a Huawei P30, a Huawei P40, and a Xiaomi MI8. The results indicate that the position and velocity accuracy can be improved in the up component using a vertical velocity constraint. The horizontal positioning accuracy was increased using a heading direction constraint with dynamic datasets. Numerically, the root mean square error (RMSE) improvement percentages were 16.77%, 14.57%, and 31.09% for HP40, HP30, and XMI8, respectively. Using an inter-smartphone distance constraint could enhance the horizontal positioning of all participating smartphones, with improvement percentages of 34.27%, 75.58%, and 23.66% for HP40, HP30, and XMI8, respectively, in the static dataset. Additionally, the improvement percentages were 15.90%, 5.55%, and 0.17% in dynamic datasets. In summary, this study demonstrates that utilizing inequality constraints can significantly improve smartphone GNSS positioning.

An optimized stochastic model for smartphone GNSS positioning

With the increasing popularity of high-precision applications of smartphone, more and more scholars carry out studies in the field of smartphone GNSS positioning. In the previous studies, more attention has been paid to data quality control, data preprocessing and observation models. However, the research on stochastic models is rare. The stochastic model is significant for the subsequent optimal positioning parameter estimation, meanwhile, the stochastic models of smartphones and professional geodetic receivers are very different for the completely different characteristics of smartphone GNSS observations. It is necessary to develop a stochastic model suitable for smartphone observations. Based on the characteristics of smartphone observations, this paper proposes an optimized stochastic model. The specific process of this method is as follows: firstly, the code-minus-phase (C-L) combi-nation and double-differenced measurement were used to quantify the noise of smartphone code and phase observations. Then, an optimized carrier-to-noise density ratio (C/N0) dependent stochastic model was proposed on the basis of the characteristic of smartphone observations. To validate the superiority of the proposed model, single point positioning (SPP) and real time kinematic (RTK) experiments were carried out by Xiaomi 8 in 2 days. The 3-dimensional root mean squares (RMS) of SPP were 6.18 and 5.38 m, with improvements varying within 0.00%-10.02% compared with the customary models. Likewise, the RMS of RTK were 0.14 and 0.24 m, with improvements fitting in range of 10.06%-39.92%. This research plays an important role in improving the positioning accuracy of smartphone and promoting the popularization of high-precision applications.

Ambiguity resolution for smartphone GNSS precise positioning: effect factors and performance

Ambiguity resolution for smartphone GNSS precise positioning: effect factors and performance

Smartphone GNSS Performance in an Urban Scenario with RAIM Application.

In an urban scenario, GNSS performance is strongly influenced by gross errors in the measurements, usually related to multipath and non-line-of-sight phenomena. The use of RAIM algorithms is a common approach to solve this issue. A significant amount of the existing GNSS receivers is currently mounted on smart devices, above all, smartphones. A typical drawback of these devices is the unavailability of raw measurements, which does not allow fully exploiting the GNSS potential; in particular, this feature limits the use of RAIM algorithms. Since 2016, for few smart devices, it has been finally possible to access GNSS raw measurements, allowing the implementation of specific algorithms and enabling new services. The Xiaomi Mi 8 is equipped with the Broadcom BCM47755 receiver, able to provide dual-frequency raw measurements from quad-constellation GPS, Glonass, Galileo, BeiDou. In this work, the performance in an urban area of the Xiaomi Mi8 GNSS was analyzed. An important issue of smartphone GNSS is related to the antenna, which is not able to protect from the multipath phenomenon; this issue has a large probability to emerge in hostile environments like urban areas. As a term of comparison, the high-sensitivity receiver NVS NV08C-CSM, connected to a patch antenna, was used. In particular, the considered receivers were placed in the same location, and their positions were estimated in single point positioning, applying a classical RAIM algorithm. An error analysis was carried out, and the obtained results demonstrated the effectiveness of RAIM when applied to Xiaomi Mi8 GNSS measurements.

Effects of ionospheric constraints in Precise Point Positioning processing of geodetic, low-cost and smartphone GNSS measurements

The proliferation of low-cost GNSS hardware smartphones providing multi-constellation, multi-frequency carrier-phase and pseudorange measurements for the Android platform offer an avenue for Precise Point Positioning (PPP) GNSS use in recent years. However, smartphone PPP is restricted due to high multipath effects and signal loss leading to metre-level accuracy and longer convergence time compared against professional-grade GNSS receivers. Proven by previous research, the combination of ionospheric constraints and receiver differential code bias (DCB) estimation is an efficient approach to improve PPP solution initialization. Therefore, this study analyzes the performance improvements by applying ionospheric constraints (IC) to PPP in comparison to conventional PPP solutions for different grades of GNSS receivers (geodetic, low-cost, and smartphone hardware) in open-sky and suburban environments. The results reveal that smartphone PPP enjoys more beneficial effects from ionospheric constraints (IC) than low-cost and geodetic hardware, as a significant 30% improvement in horizontal rms is observed in the first epoch of examined datasets, and a 57% improvement in the first 20 min of data collection. For low-cost and geodetic equipment, the first epoch improvement is only 21% and 18%, respectively. Furthermore, a 44% convergence time reduction for smartphone PPP-IC is observed to attain 1 m level of horizontal accuracy. Based on these investigations, PPP-IC can provide greater impact for those low-quality measurements before convergence, showing potential in achieving fast initialization and accurate positioning within a short time for autonomous vehicles, precision agriculture, and smartphone location-based services.

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

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 -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 -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.

Integration of Smartphones Into Small Unmanned Aircraft Systems to Sense Water in Soil by Using Reflected GPS Signals

We investigate the feasibility of using built-in GNSS sensors within ubiquitous smartphone devices from a small UAS for the purpose of land remote sensing. We summarize the experimental findings and challenges that need to be resolved in order to perform the GNSS reflectometry (GNSS-R) technique via smartphones. In late 2018, a series of experiments were conducted and designed by integrating two smartphones into a multicopter UAS by attaching them to ground plates to isolate and record both direct and reflected GNSS carrier-to-noise density ratio ( $C/N_0$ ) separately. It was demonstrated that, first, fluctuations of moving GNSS specular reflections are correlated with spatial ground features with appreciable dynamic range and second, radiation pattern of the smartphone's inbuilt antenna has a significant effect on the received signal strength. In 2020, more experiments were conducted to examine the quality of in-built chip and antenna of a smartphone with regard to the GNSS-R approach as well as the consistency of measurements. These follow-up experiments involved, first, placement of the smartphone on a pan-tilt mechanism on a tripod, second, formation flights with smartphone on a gimbal and a high-quality custom-built dual-channel GNSS-R receiver, and, third, flying the UAS at different times of the day on two consecutive days. It was demonstrated that, first, the radiation pattern of the smartphone's GNSS antenna are observed to be highly irregular, but time-invariant, and, second, internal GNSS chip produces observables of sufficient quality, and, third, the fluctuations of the reflected signal are repeatable under the same configuration at different times.

GNSS code and carrier phase observations of a Huawei P30 smartphone: quality assessment and centimeter-accurate positioning

In 2016, an application programming interface was added to the Android operating systems, which enables the access of GNSS raw observations. Since then, an in-depth evaluation of the performance of smartphone GNSS chips is very much simplified. We analyzed the quality of the GNSS observations, especially the carrier phase observations, of the dual-frequency GNSS chip Kirin 980 built into Huawei P30 and other smartphones. More than 80 h of static observations were collected at several locations. The code and carrier phase observations were processed in baseline mode with reference to observations of geodetic-grade equipment. We were able to fix carrier phase ambiguities for GPS L1 observations. Furthermore, we performed an antenna calibration for this frequency, which revealed that the horizontal phase center offsets from the central vertical axis of the smartphone and also the phase center variations do not exceed 1–2 cm. After successful ambiguity fixing, the 3D position errors (standard deviations) are smaller 4 cm after 5 min of static observation session and 2 cm for long observation session.

Signal characterization and assessment of code GNSS positioning with low-power consumption smartphones

It has been acknowledged that smartphone GNSS observations suffer not only from high measurement noise and multipath but also from anomalies such as duty cycling and gradual accumulation of phase errors. These phenomena importantly constrain the application of smartphone phase measurement to high-precision techniques such as RTK or PPP. Hence, we aim at a comprehensive characterization of smartphone signal quality, including carrier-to-noise density ratio, measurement noise and anomalies present in observables with the focus on the impact of duty-cycling mode. The analysis confirms the abnormal properties of smartphone measurements related to the divergence between code and phase data and poor quality of the latter. To address these limitations, the second objective is to assess the smartphone medium- to long-range code-based relative positioning. This task includes the validation of the weighting scheme suited for handling the low quality of smartphone observations. The results show that it is feasible to use a sparse countrywide GNSS network as reference stations for code-based relative positioning. Even with the baselines over 100 km, we can significantly enhance the positioning with respect to a stand-alone solution and reach the submeter level of horizontal coordinate accuracy. We have also noticed a discernible benefit from the C/N0-dependent weighting scheme, which is superior to the satellite elevation one.

Exploring GNSS Crowdsourcing Feasibility: Combinations of Measurements for Modeling Smartphone and Higher End GNSS Receiver Performance.

GNSS receiver data crowdsourcing is of interest for multiple applications, e.g., weather monitoring. The bottleneck in this technology is the quality of the GNSS receivers. Therefore, we lay out in an introductory manner the steps to estimate the performance of an arbitrary GNSS receiver via the measurement errors related to its instrumentation. Specifically, we do not need to know the position of the receiver antenna, which allows also for the assessment of smartphone GNSS receivers having integrated antennas. Moreover, the method is independent of atmospheric errors so that no ionospheric or tropospheric correction services provided by base stations are needed. Error models for performance evaluation can be calculated from receiver RINEX (receiver independent exchange format)data using only ephemeris corrections. For the results, we present the quality of different receiver grades through parametrized error models that are likely to be helpful in stochastic modeling, e.g., for Kalman filters, and in assessing GNSS receiver qualities for crowdsourcing applications. Currently, the typical positioning precision for the latest smartphone receivers is around the decimeter level, while for a professional-grade receiver, it is within a few millimeters.

Horizontal accuracy and applicability of smartphone GNSS positioning in forests

Horizontal accuracy and applicability of smartphone GNSS positioning in forests