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1
Skorupa, Bogdan. "The problem of GNSS positioning with measurements recorded using Android mobile devices." Budownictwo i Architektura 18, no. 3 (January 24, 2020): 051–62. http://dx.doi.org/10.35784/bud-arch.738.
Повний текст джерелаАнотація:
The current work presents the issue of determining the position of the observer using measurements registered with GNSS (Global Navigation Satellite System) receivers that Android mobile devices are equipped with. The discussed questions concern using GNSS measurement data, which have been made available in the Android system since version 7.0. The present paper has the character of a review. It demonstrates how measurement data can be obtained via Application Programming Interface. Moreover, it discusses the available software that can be for registering measurements and their initial analysis. Subsequently, it reviews scientific works concerning the problem of positioning with the use of smartphones. Special emphasis was placed on tests consisting in an analysis of phase observations registered using dual-frequency receivers. The summary of the article presents the prospects for using mobile devices in precise point positioning. It also points out the limitations to achieving high accuracy and reliability of such measurements.
2
Fortunato, Marco, Michela Ravanelli, and Augusto Mazzoni. "Real-Time Geophysical Applications with Android GNSS Raw Measurements." Remote Sensing 11, no. 18 (September 11, 2019): 2113. http://dx.doi.org/10.3390/rs11182113.
Повний текст джерелаАнотація:
The number of Android devices enabling access to raw GNSS (Global Navigation Satellite System) measurements is rapidly increasing, thanks to the dedicated Google APIs. In this study, the Xiaomi Mi8, the first GNSS dual-frequency smartphone embedded with the Broadcom BCM47755 GNSS chipset, was employed by leveraging the features of L5/E5a observations in addition to the traditional L1/E1 observations. The aim of this paper is to present two different smartphone applications in Geoscience, both based on the variometric approach and able to work in real time. In particular, tests using both VADASE (Variometric Approach for Displacement Analysis Stand-alone Engine) to retrieve the 3D velocity of a stand-alone receiver in real-time, and VARION (Variometric Approach for Real-Time Ionosphere Observations) algorithms, able to reconstruct real-time sTEC (slant total electron content) variations, were carried out. The results demonstrate the contribution that mass-market devices can offer to the geosciences. In detail, the noise level obtained with VADASE in a static scenario—few mm/s for the horizontal components and around 1 cm/s for the vertical component—underlines the possibility, confirmed from kinematic tests, of detecting fast movements such as periodic oscillations caused by earthquakes. VARION results indicate that the noise level can be brought back to that of geodetic receivers, making the Xiaomi Mi8 suitable for real-time ionosphere monitoring.
3
Weng, Duojie, Xingli Gan, Wu Chen, Shengyue Ji, and Yangwei Lu. "A New DGNSS Positioning Infrastructure for Android Smartphones." Sensors 20, no. 2 (January 15, 2020): 487. http://dx.doi.org/10.3390/s20020487.
Повний текст джерелаАнотація:
One’s position has become an important piece of information for our everyday lives in a smart city. Currently, a position can be obtained easily using smartphones that is equipped with low-cost Global Navigation Satellite System (GNSS) chipsets with accuracy varying from 5 m to 10 m. Differential GNSS (DGNSS) is an efficient technology that removes the majority of GNSS errors with the aid of reference stations installed at known locations. The sub-meter accuracy can be achieved when applying the DGNSS technology on the advanced receivers. In 2016, Android has opened the accesses of raw GNSS measurements to developers. However, most of the mid and low-end smartphones only provide the data using the National Marine Electronics Association (NMEA) protocol. They do not provide the raw measurements, and thus do not support the DGNSS operation either. We proposed a DGNSS infrastructure that correct the standalone GNSS position of smartphones using the corrections from the reference station. In the infrastructure, the position correction is generated considering the GNSS satellite IDs that contribute to the standalone solution in smartphones, and the position obtained is equivalent to the solution of using the range-domain correction directly. To serve a large number of smartphone users, a Client/Server architecture is developed to cope with a mass of DGNSS positioning requests efficiently. The comparison of the proposed infrastructure against the ground truth, for all field tests in open areas, showed that the infrastructure achieves the horizontal positioning accuracy better than 2 m. The improvement in accuracy can reach more than 50% for the test in the afternoon. The infrastructure brings benefits to applications that require more accuracy without requiring any hardware modifications.
4
Yun, Jeonghyeon, Cheolsoon Lim, and Byungwoon Park. "Inherent Limitations of Smartphone GNSS Positioning and Effective Methods to Increase the Accuracy Utilizing Dual-Frequency Measurements." Sensors 22, no. 24 (December 15, 2022): 9879. http://dx.doi.org/10.3390/s22249879.
Повний текст джерелаАнотація:
Xiaomi Mi8 with a Broadcom BCM47755 chip, an Android smartphone that supports multi-constellation (GPS, GLONASS, Galileo, BeiDou, and QZSS) and dual-frequency (L1/E1 and L5/E5), was launched in May 2018. Unlike previously released smartphones, it was technically expected to provide robust precise positioning with a fast ambiguity resolution, which led many researchers to be overly optimistic about the applicability of high-accuracy techniques such as real-time kinematic (RTK) systems and precise point positioning (PPP) of smartphones. The global navigation satellite system (GNSS) raw measurement quality of Android smartphones is, however, inherently far lower than that of general GNSS receivers due to their structure, which accordingly makes it difficult for them to be realized. Considering inherent limitations of smartphones such as low-quality antenna, frequent cycle slips, and the duty cycle, a practical strategy including L5 measurements, pseudo-range corrections for L5, and a weighting method is proposed in this paper. The results show that the proposed methods of L5 differential GNSS (DGNSS) and Doppler-based filtering can guarantee a positioning accuracy of 1.75 m horizontally and 4.56 m vertically in an Android device, which is comparable to the performance of commercial low-cost receivers.
5
Gogoi, Neil, Alex Minetto, Nicola Linty, and Fabio Dovis. "A Controlled-Environment Quality Assessment of Android GNSS Raw Measurements." Electronics 8, no. 1 (December 21, 2018): 5. http://dx.doi.org/10.3390/electronics8010005.
Повний текст джерелаАнотація:
Raw Global Navigation Satellite System (GNSS) measurements have been available since 2016 in select Android smartphones. The availability of such observations allows smartphones users, in principle, to significantly improve the quality of GNSS-based positioning by applying customized and advanced positioning algorithms. However, the quality of such measurements is poor, mainly because of the low quality of smartphone hardware components and the nonideal environment in which phones are typically used. To overcome this problem and to separate the contribution of the hardware components and signal quality, dedicated test campaigns were carried out in a real environment and in a controlled-environment anechoic chamber using several different Android models. In addition, signal-processing techniques aimed at increasing the accuracy and precision of the solution were employed. Results show that the quality of the data captured in the anechoic chamber was significantly better than in real conditions. Furthermore, such analysis allows to underline certain phenomena in smartphones, such as the duty cycle, and to test the validity of anechoic environments for Android raw measurements.
6
Benvenuto, Lorenzo, Paolo Dabove, Ilaria Ferrando, and Domenico Sguerso. "Preliminary Results on Tropospheric ZTD Estimation by Smartphone." Remote Sensing 13, no. 22 (November 13, 2021): 4567. http://dx.doi.org/10.3390/rs13224567.
Повний текст джерелаАнотація:
The Global Navigation Satellite System (GNSS) receiver is one of the many sensors embedded in smartphones. The early versions of the Android operating system could only access limited information from the GNSS, allowing the related Application Program Interface (API) to obtain only the location. With the development of the Android 7.0 (Nougat) operating system in May 2016, raw measurements from the internal GNSS sensor installed in the smartphone could be accessed. This work aims to show an initial analysis regarding the feasibility of Zenith Total Delay (ZTD) estimation by GNSS measurements extracted from smartphones, evaluating the accuracy of estimation to open a new window on troposphere local monitoring. Two different test sites have been considered, and two different types of software for data processing have been used. ZTDs have been estimated from both a dual-frequency and a multi-constellation receiver embedded in the smartphone, and from a GNSS Continuously Operating Reference Station (CORS). The results have shown interesting performances in terms of ZTD estimation from the smartphone in respect of the estimations obtained with a geodetic receiver.
7
Everett, Tim, Trey Taylor, Dong-Kyeong Lee, and Dennis M. Akos. "Optimizing the Use of RTKLIB for Smartphone-Based GNSS Measurements." Sensors 22, no. 10 (May 18, 2022): 3825. http://dx.doi.org/10.3390/s22103825.
Повний текст джерелаАнотація:
The Google Smartphone Decimeter Challenge (GSDC) was a competition held in 2021, where data from a variety of instruments useful for determining a phone’s position (signals from GPS satellites, accelerometer readings, gyroscope readings, etc.) using Android smartphones were provided to be processed/assessed in regard to the most accurate determination of the longitude and latitude of user positions. One of the tools that can be utilized to process the GNSS measurements is RTKLIB. RTKLIB is an open-source GNSS processing software tool that can be used with the GNSS measurements, including code, carrier, and doppler measurements, to provide real-time kinematic (RTK), precise point positioning (PPP), and post-processed kinematic (PPK) solutions. In the GSDC, we focused on the PPK capabilities of RTKLIB, as the challenge only required post-processing of past data. Although PPK positioning is expected to provide sub-meter level accuracies, the lower quality of the Android measurements compared to geodetic receivers makes this performance difficult to achieve consistently. Another latent issue is that the original RTKLIB created by Tomoji Takasu is aimed at commercial GNSS receivers rather than smartphones. Therefore, the performance of the original RTKLIB for the GSDC is limited. Consequently, adjustments to both the code-base and the default settings are suggested. When implemented, these changes allowed RTKLIB processing to score 5th place, based on the performance submissions of the prior GSDC competition. Detailed information on what was changed, and the steps to replicate the final results, are presented in the paper. Moreover, the updated code-base, with all the implemented changes, is provided in the public repository. This paper outlines a procedure to optimize the use of RTKLIB for Android smartphone measurements, highlighting the changes needed given the low-quality measurements from the mobile phone platform (relative to the survey grade GNSS receiver), which can be used as a basis point for further optimization for future GSDC competitions.
8
Hu, Jiahuan, Ding Yi, and Sunil Bisnath. "A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments." Sensors 23, no. 3 (February 2, 2023): 1631. http://dx.doi.org/10.3390/s23031631.
Повний текст джерелаАнотація:
Precise positioning using smartphones has been a topic of interest especially after Google decided to provide raw GNSS measurement through their Android platform. Currently, the greatest limitations in precise positioning with smartphone Global Navigation Satellite System (GNSS) sensors are the quality and availability of satellite-to-smartphone ranging measurements. Many papers have assessed the quality of GNSS pseudorange and carrier-phase measurements in various environments. In addition, there is growing research in the inclusion of a priori information to model signal blockage, multipath, etc. In this contribution, numerical estimation of actual range errors in smartphone GNSS precise positioning in realistic environments is performed using a geodetic receiver as a reference. The range errors are analyzed under various environments and by placing smartphones on car dashboards and roofs. The distribution of range errors and their correlation to prefit residuals is studied in detail. In addition, a comparison of range errors between different constellations is provided, aiming to provide insight into the quantitative understanding of measurement behavior. This information can be used to further improve measurement quality control, and optimize stochastic modeling and position estimation processes.
9
Dabove, Paolo, Vincenzo Di Pietra, and Marco Piras. "GNSS Positioning Using Mobile Devices with the Android Operating System." ISPRS International Journal of Geo-Information 9, no. 4 (April 7, 2020): 220. http://dx.doi.org/10.3390/ijgi9040220.
Повний текст джерелаАнотація:
The access and the use of the global navigation satellite system (GNSS) pseudo-range and carrier-phase measurements mobile devices as smartphones and tablets with an Android operating system has transformed the concept of accurate positioning with mobile devices. In this work, the comparison of positioning performances obtained with a smartphone and an external mass-market GNSS receiver both in real-time and post-processing is made. Particular attention is also paid to accuracy and precision of positioning results, also analyzing the possibility of estimating the phase ambiguities as integer values (fixed positioning) that it is still challenging for mass-market devices. The precisions and accuracies obtained with the mass-market receiver were about 5 cm and 1 cm both for real-time and post-processing solutions, respectively, while those obtained with a smartphone were slightly worse (few meters in some cases) due to the noise of its measurements.
10
Chen, Bo, Chengfa Gao, Yongsheng Liu, and Puyu Sun. "Real-time Precise Point Positioning with a Xiaomi MI 8 Android Smartphone." Sensors 19, no. 12 (June 25, 2019): 2835. http://dx.doi.org/10.3390/s19122835.
Повний текст джерелаАнотація:
The Global Navigation Satellite System (GNSS) positioning technology using smartphones can be applied to many aspects of mass life, and the world’s first dual-frequency GNSS smartphone Xiaomi MI 8 represents a new trend in the development of GNSS positioning technology with mobile phones. The main purpose of this work is to explore the best real-time positioning performance that can be achieved on a smartphone without reference stations. By analyzing the GNSS raw measurements, it is found that all the three mobile phones tested have the phenomenon that the differences between pseudorange observations and carrier phase observations are not fixed, thus a PPP (precise point positioning) method is modified accordingly. Using a Xiaomi MI 8 smartphone, the modified real-time PPP positioning strategy which estimates two clock biases of smartphone was applied. The results show that using multi-GNSS systems data can effectively improve positioning performance; the average horizontal and vertical RMS positioning error are 0.81 and 1.65 m respectively (using GPS, BDS, and Galileo data); and the time required for each time period positioning errors in N and E directions to be under 1 m is less than 30s.
11
Geng, Jianghui, Enming Jiang, Guangcai Li, Shaoming Xin, and Na Wei. "An Improved Hatch Filter Algorithm towards Sub-Meter Positioning Using only Android Raw GNSS Measurements without External Augmentation Corrections." Remote Sensing 11, no. 14 (July 15, 2019): 1679. http://dx.doi.org/10.3390/rs11141679.
Повний текст джерелаАнотація:
In May 2016, the availability of GNSS raw measurements on smart devices was announced by Google with the release of Android 7. It means that developers can access carrier-phase and pseudorange measurements and decode navigation messages for the first time from mass-market Android-devices. In this paper, an improved Hatch filter algorithm, i.e., Three-Thresholds and Single-Difference Hatch filter (TT-SD Hatch filter), is proposed for sub-meter single point positioning with raw GNSS measurements on Android devices without any augmentation correction input, where the carrier-phase smoothed pseudorange window width adaptively varies according to the three-threshold detection for ionospheric cumulative errors, cycle slips and outliers. In the mean time, it can also eliminate the inconsistency of receiver clock bias between pseudorange and carrier-phase by inter-satellite difference. To eliminate the effects of frequent smoothing window resets, we combine TT-SD Hatch filter and Kalman filter for both time update and measurement update. The feasibility of the improved TT-SD Hatch filter method is then verified using static and kinematic experiments with a Nexus 9 Android tablet. The result of the static experiment demonstrates that the position RMS of TT-SD Hatch filter is about 0.6 and 0.8 m in the horizontal and vertical components, respectively. It is about 2 and 1.6 m less than the GNSS chipset solutions, and about 10 and 10 m less than the classical Hatch filter solution, respectively. Moreover, the TT-SD Hatch filter can accurately detect the cycle slips and outliers, and reset the smoothed window in time. It thus avoids the smoothing failure of Hatch filter when a large cycle-slip or an outlier occurs in the observations. Meanwhile, with the aid of the Kalman filter, TT-SD Hatch filter can keep continuously positioning at the sub-meter level. The result of the kinematic experiment demonstrates that the TT-SD Hatch filter solution can converge after a few minutes, and the 2D error is about 0.9 m, which is about 64%, 89%, and 92% smaller than that of the chipset solution, the traditional Hatch filter solution and standard single point solution, respectively. Finally, the TT-SD Hatch filter solution can recover a continuous driving track in this kinematic test.
12
Zangenehnejad, Farzaneh, Yang Jiang, and Yang Gao. "GNSS Observation Generation from Smartphone Android Location API: Performance of Existing Apps, Issues and Improvement." Sensors 23, no. 2 (January 10, 2023): 777. http://dx.doi.org/10.3390/s23020777.
Повний текст джерелаАнотація:
Precise position information available from smartphones can play an important role in developing new location-based service (LBS) applications. Starting from 2016, and after the release of Nougat version (Version 7) by Google, developers have had access to the GNSS raw measurements through the new application programming interface (API), namely android.location (API level 24). However, the new API does not provide the typical GNSS observations directly (e.g., pseudorange, carrier-phase and Doppler observations) which have to be generated by the users themselves. Although several Apps have been developed for the GNSS observations generation, various data analyses indicate quality concerns, from biases to observation inconsistency in the generated GNSS observations output from those Apps. The quality concerns would subsequently affect GNSS data processing such as cycle slip detection, code smoothing and ultimately positioning performance. In this study, we first investigate algorithms for GNSS observations generation from the android.location API output. We then evaluate the performances of two widely used Apps (Geo++RINEX logger and GnssLogger Apps), as well as our newly developed one (namely UofC CSV2RINEX tool) which converts the CSV file to a Receiver INdependent Exchange (RINEX) file. Positioning performance analysis is also provided which indicates improved positioning accuracy using our newly developed tool. Future work finding out the potential reasons for the identified misbehavior in the generated GNSS observations is recommended; it will require a joint effort with the App developers.
13
Angrisano, Antonio, Giovanni Cappello, Silvio Del Pizzo, and Salvatore Gaglione. "Time-Differenced Carrier Phase Technique for Precise Velocity Estimation on an Android Smartphone." Sensors 22, no. 21 (November 4, 2022): 8514. http://dx.doi.org/10.3390/s22218514.
Повний текст джерелаАнотація:
GNSS (Global Navigation Satellite System) receivers are not only able to accurately determine position, but also velocity, knowledge of which could be important in several applications. The most adopted technique for velocity estimation exploits the Doppler shift due to the relative motion between the signal source and the receiver. Alternatively, the TDCP (Time-Differenced Carrier Phase) technique, based on the differences between consecutive carrier-phase measurements, can be used. TDCP is theoretically able to achieve better performance compared with the Doppler-based approach, exploiting the high precision of a carrier-phase observable, and without suffering the ambiguity issue. The main objective of this study is to analyze TDCP performance on a smartphone GNSS chip. Smartphones GNSS receivers are usually characterized by noisy observables owing to the low quality of the antenna used; it is, therefore, interesting to compare the smartphone TDCP performance with that of the Doppler-based technique. To evaluate the benefits that TDCP can provide, especially in terms of the smartphone chip, these two approaches to velocity determination are compared using three different devices: a Novatel geodetic receiver, a u-blox multi-frequency receiver, and a Xiaomi Mi8 smartphone. The results demonstrate a performance degradation in the smartphone GNSS chip when TDCP is used, compared with the performance of higher-grade receivers. In fact, the Xiaomi Mi8 maximum errors are greater than those of the Novatel geodetic receiver, but they are still acceptable as they do not exceed 6 cm/s, making the TDCP technique a valid approach for advanced algorithms; indeed, TDCP velocity demonstrates a few mm/s accuracy with a smartphone. The application of a RAIM algorithm enables error reduction and the achievement of reliable information; the obtained solution reliability is about 89%.
14
Lachapelle, Gérard, Paul Gratton, Jamie Horrelt, Erica Lemieux, and Ali Broumandan. "Evaluation of a Low Cost Hand Held Unit with GNSS Raw Data Capability and Comparison with an Android Smartphone." Sensors 18, no. 12 (November 29, 2018): 4185. http://dx.doi.org/10.3390/s18124185.
Повний текст джерелаАнотація:
A newly available portable unit with GNSS raw data recording capability is assessed to determine static and kinematic position accuracy in various environments. This unit is the GPSMap 66, introduced by Garmin in early September. It is all-weather and robust for field use, and comes with a helix antenna. The high sensitivity chipset is capable of acquiring and tracking signals in highly attenuated environments. It can track single frequency GPS, GPS + GLONASS or GPS + Galileo and record code, Doppler and carrier phase data every second in the RINEX format. The evaluation presented herein focusses on GPS and Galileo. Static and kinematic test results obtained under a wide range of realistic field conditions are reported. Differential GNSS methods and Precise Point Positioning (PPP) are used to assess absolute position accuracy in ITRF coordinates, which is sufficiently close to the GPS and Galileo reference frame for the current purpose. Under low multipath conditions, measurements are found to be sufficiently accurate to provide single epoch, bias free position accuracy of a few metres. Accuracy is a function of signal attenuation and multipath conditions. The use of an external geodetic antenna significantly reduces measurement noise and multipath in high multipath environments. Carrier phase measurements, available more or less continuously under open sky conditions, significantly improve performance in differential mode. Accuracy in vehicular mode using code and carrier phase differential RTK solution is at the level of a few to several dm. Tests were conducted in parallel with a Huawei P10 Android 8.0 smartphone. The code measurement noise of this unit was found to be significantly higher than that of the GPSMap 66, a major reason being its lower performance PIFA antenna; carrier phase was only available for short time intervals, significantly degrading differential position accuracy performance.
15
Wang, Jin, and Jun Luo. "No Perfect Outdoors: Towards a Deep Profiling of GNSS-Based Location Contexts." Future Internet 14, no. 1 (December 23, 2021): 7. http://dx.doi.org/10.3390/fi14010007.
Повний текст джерелаАнотація:
While both outdoor and indoor localization methods are flourishing, how to properly marry them to offer pervasive localizability in urban areas remains open. Recently, proposals on indoor–outdoor detection have made the first step towards such an integration, yet complicated urban environments render such a binary classification incompetent. Fortunately, the latest developments in Android have granted us access to raw GNSS measurements, which contain far more information than commonly derived GPS location indicators. In this paper, we explore these newly available measurements in order to better characterize diversified urban environments. Essentially, we tackle the challenges introduced by the complex GNSS data and apply a deep learning model to identify representations for respective location contexts. We further develop two preliminary applications of our deep profiling: one, we offer a more fine-grained semantic classification than binary indoor–outdoor detection; and two, we derive a GPS error indicator that is more meaningful than that provided by Google Maps. These results are all corroborated by our extensive data collection and trace-driven evaluations.
16
Pepe, Massimiliano. "CORS ARCHITECTURE AND EVALUATION OF POSITIONING BY LOW-COST GNSS RECEIVER." Geodesy and cartography 44, no. 2 (August 8, 2018): 36–44. http://dx.doi.org/10.3846/gac.2018.1255.
Повний текст джерелаАнотація:
In recent years, the use of low cost GNSS receivers is becoming widespread due to their increasing performance in the spatial positioning, flexibility, ease of use and really interesting price. In addition, a recent technique of Global Navigation Satellite System (GNSS) survey, called Network Real Time Kinematic (NRTK), allows to obtain to rapid and accurate positioning measurements. The main feature of this approach is to use the raw measurements obtained and stored from a network of Continuously Operating Reference Stations (CORS) in order to generate more reliable error models that can mitigate the distance-dependent errors within the area covered by the CORS. Also, considering the huge potential of this GNSS positioning system, the purpose of this paper is to analyze and investigate the performance of the NTRK approach using a low cost GNSS receiver, in stop-and-go kinematic technique. By several case studies it was shown that, using a low cost RTK board for Arduino environment, a smartphone with open source application for Android and the availability of data correction from CORS service, a quick and accurate positioning can be obtained. Because the measures obtained in this way are quite noisy and, more in general, increasing with the baseline, by a simple and suitable statistic treatment, it was possible to increase the quality of the measure. In this way, this low cost architecture could be applied in many geomatics fields. In addition to presenting the main aspects of the NTRK infrastructure and a review of several types of correction, a general workflow in order to obtain quality data in NRTK mode, regardless of the type of GNSS receiver (multi constellations, single or many frequencies, etc.) is discussed.
17
Robustelli, Umberto, Valerio Baiocchi, and Giovanni Pugliano. "Assessment of Dual Frequency GNSS Observations from a Xiaomi Mi 8 Android Smartphone and Positioning Performance Analysis." Electronics 8, no. 1 (January 15, 2019): 91. http://dx.doi.org/10.3390/electronics8010091.
Повний текст джерелаАнотація:
On May 2018 the world’s first dual-frequency Global Navigation Satellite System (GNSS) smartphone produced by Xiaomi equipped with a Broadcom BCM47755 chip was launched. It is able to receive L1/E1/ and L5/E5 signals from GPS, Galileo, Beidou, and GLONASS (GLObal NAvigation Satellite System) satellites. The main aim of this work is to achieve the phone’s position by using multi-constellation, dual frequency pseudorange and carrier phase raw data collected from the smartphone. Furthermore, the availability of dual frequency raw data allows to assess the multipath performance of the device. The smartphone’s performance is compared with that of a geodetic receiver. The experiments were conducted in two different scenarios to test the smartphone under different multipath conditions. Smartphone measurements showed a lower C/N0 and higher multipath compared with those of the geodetic receiver. This produced negative effects on single-point positioning as showed by high root mean square error (RMS). The best positioning accuracy for single point was obtained with the E5 measurements with a DRMS (horizontal root mean square error) of 4.57 m. For E1/L1 frequency, the 2DRMS was 5.36 m. However, the Xiaomi Mi 8, thanks to the absence of the duty cycle, provided carrier phase measurements used for a static single frequency relative positioning with an achieved 2DRMS of 1.02 and 1.95 m in low and high multipath sites, respectively.
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Friad Qadr, Runahi, Halgurd S. Maghdid, and Azhin T. Sabir. "Novel Integration of Wi-Fi Signal and Magnetometer Sensor Measurements in Fingerprinting Technique for Indoors Smartphone positioning." ITM Web of Conferences 42 (2022): 01016. http://dx.doi.org/10.1051/itmconf/20224201016.
Повний текст джерелаАнотація:
Smartphones are becoming more widespread, and location-based services (LBS) have become one of the most important uses in people’s daily lives. While outdoor location is reasonably simple thanks to GNSS signals, however, indoor location is more problematic due to the lack of GNSS signals. As a result of the widespread deployment of alternative technologies such as wireless and sensors technologies, various studies on wireless-based indoor positioning have been conducted. However, each technology has its own limitations including multipath fading of wireless signals causes time-varying received signal strength as well as the accumulated error of the onboard sensors (i.e. sensor drift) resulting in poor localization accuracy. Motivated by these restrictions, this work integrates the applicability of two technologies for indoor positioning that are already available in smartphones by avoiding their limitation. The integration is based on fingerprinting-positioning technique by including magnetometer sensor measurements and WiFi signal strength. Android-based smartphones with low-cost sensors in real indoor scenarios are utilized to create a dataset and collect independent track tests to confirm results. The performance of different scenarios, such as Wi-Fi alone, magnetometer alone, and magnetometer-aided Wi-Fi, is compared. The experimental results show that the combination of magnetometer sensor and WiFi signal strength provides significant results in which leads to reducing the location error to 0.7224 meters.
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Niu, Zun, Fugui Guo, Qiangqiang Shuai, Guangchen Li, and Bocheng Zhu. "The Integration of GPS/BDS Real-Time Kinematic Positioning and Visual–Inertial Odometry Based on Smartphones." ISPRS International Journal of Geo-Information 10, no. 10 (October 14, 2021): 699. http://dx.doi.org/10.3390/ijgi10100699.
Повний текст джерелаАнотація:
The real-time kinematic positioning technique (RTK) and visual–inertial odometry (VIO) are both promising positioning technologies. However, RTK degrades in GNSS-hostile areas, where global navigation satellite system (GNSS) signals are reflected and blocked, while VIO is affected by long-term drift. The integration of RTK and VIO can improve the accuracy and robustness of positioning. In recent years, smartphones equipped with multiple sensors have become commodities and can provide measurements for integrating RTK and VIO. This paper verifies the feasibility of integrating RTK and VIO using smartphones, and we propose an improved algorithm to integrate RTK and VIO with better performance. We began by developing an Android smartphone application for data collection and then wrote a Python program to convert the data to a robot operating system (ROS) bag. Next, we established two ROS nodes to calculate the RTK results and accomplish the integration. Finally, we conducted experiments in urban areas to assess the integration of RTK and VIO based on smartphones. The results demonstrate that the integration improves the accuracy and robustness of positioning and that our improved algorithm reduces altitude deviation. Our work can aid navigation and positioning research, which is the reason why we open source the majority of the codes at our GitHub.
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Magalhães, Américo, Luísa Bastos, Dalmiro Maia, and José Alberto Gonçalves. "Relative Positioning in Remote Areas Using a GNSS Dual Frequency Smartphone." Sensors 21, no. 24 (December 14, 2021): 8354. http://dx.doi.org/10.3390/s21248354.
Повний текст джерелаАнотація:
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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Gioia, Ciro, and Daniele Borio. "NeQuick-G and Android Devices: A Compromise between Computational Burden and Accuracy." Sensors 20, no. 20 (October 19, 2020): 5908. http://dx.doi.org/10.3390/s20205908.
Повний текст джерелаАнотація:
Ionospheric delay is one of the largest errors affecting Global Navigation Satellite System (GNSS) positioning in open-sky conditions, and different methods are currently available for mitigating ionospheric effects including dual-frequency measurements and corrections from augmentation systems. For single-frequency standalone receivers, the most widely used approach to correct ionospheric delays is to rely on a model. In this respect, Klobuchar and NeQuick-G Ionospheric Correction Algorithms (ICAs) are the approaches adopted by GPS and Galileo, respectively. While the latter outperforms the Klobuchar model, it requires a significantly higher computational load, which can limit its exploitation in some market segments such as smartphones. In order to foster adoption of the NeQuick-G model in this type of device, a smart application of NeQuick-G is proposed. The solution relies on the assumption that ionospheric delays are practically constant over short time intervals. Thus, the update rate of the ionospheric correction computation can be significantly reduced. This solution was implemented, tested, and evaluated using real data collected with a static smartphone in an ad hoc set-up. The impact of reducing the ionospheric correction update rate has been evaluated in terms of processing time, of ionospheric correction deviations and in the Ranging Error (RE) and position domains. The analysis shows that a significant reduction of the processing time can be obtained with negligible degradation of the navigation solution.
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Zhu, Yida, Haiyong Luo, Qu Wang, Fang Zhao, Bokun Ning, Qixue Ke, and Chen Zhang. "A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning." Sensors 19, no. 4 (February 14, 2019): 786. http://dx.doi.org/10.3390/s19040786.
Повний текст джерелаАнотація:
The widespread popularity of smartphones makes it possible to provide Location-Based Services (LBS) in a variety of complex scenarios. The location and contextual status, especially the Indoor/Outdoor switching, provides a direct indicator for seamless indoor and outdoor positioning and navigation. It is challenging to quickly detect indoor and outdoor transitions with high confidence due to a variety of signal variations in complex scenarios and the similarity of indoor and outdoor signal sources in the IO transition regions. In this paper, we consider the challenge of switching quickly in IO transition regions with high detection accuracy in complex scenarios. Towards this end, we analyze and extract spatial geometry distribution, time sequence and statistical features under different sliding windows from GNSS measurements in Android smartphones and present a novel IO detection method employing an ensemble model based on stacking and filtering the detection result by Hidden Markov Model. We evaluated our algorithm on four datasets. The results showed that our proposed algorithm was capable of identifying IO state with 99.11% accuracy in indoor and outdoor environment where we have collected data and 97.02% accuracy in new indoor and outdoor scenarios. Furthermore, in the scenario of indoor and outdoor transition where we have collected data, the recognition accuracy reaches 94.53% and the probability of switching delay within 3 s exceeds 80%. In the new scenario, the recognition accuracy reaches 92.80% and the probability of switching delay within 4 s exceeds 80%.
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Falkowski-Gilski, Przemysław. "QUALITY OF SATELLITE COMMUNICATION IN SELECTED MOBILE ANDROID SMARTPHONES." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 11, no. 4 (December 20, 2021): 32–37. http://dx.doi.org/10.35784/iapgos.2751.
Повний текст джерелаАнотація:
Today, thanks to mobile devices, satellite communication is available to anyone and everywhere. Gaining information on one’s position using GNSS (Global Navigation Satellite Systems), particularly in unknown urban environments, had become an everyday activity. With the widespread of mobile devices, particularly smartphones, each person can obtain information considering his or her location anytime and everywhere. This paper is focused on a study, considering the quality of satellite communication in case of selected mobile terminals. It describes a measurement campaign carried out in varying urban environments, including a set of Android-powered smartphones coming from different manufacturers. Based on this, respective conclusions and remarks are given, which can aid consumers as well as device manufacturers and application developers.
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Yan, Wenlin, Qiuzhao Zhang, Lijuan Wang, Ying Mao, Aisheng Wang, and Changsheng Zhao. "A Modified Kalman Filter for Integrating the Different Rate Data of Gyros and Accelerometers Retrieved from Android Smartphones in the GNSS/IMU Coupled Navigation." Sensors 20, no. 18 (September 12, 2020): 5208. http://dx.doi.org/10.3390/s20185208.
Повний текст джерелаАнотація:
Recent study indicates that by using the inertial measurement unit (IMU) sensors inside smartphones, we can obtain similar navigation solutions to the professional ones. However, the sampling rates of the gyros and accelerometers inside some types of smartphones are not set in the same frequencies, i.e., the gyros of “Huawei p40” are in 50 Hz while the accelerometer is 100 Hz. The conventional method is resampling the higher frequency to the lower frequency ones, which means the resampled accelerometer will lose half frequency observations. In this work, a modified Kalman filter was proposed to integrate all these different rate IMU data in the GNSS/IMU-smartphone coupled navigation. To validate the proposed method, a terrestrial test with two different types of android smartphones was done. With the proposed method, a slight improvement of the attitude solutions can be seen in the experiments under the GNSS open-sky condition, and the obvious improvement of the attitude solutions can be witnessed at the simulated GNSS denied situation. The improvements by 45% and 23% of the horizontal position accuracy can be obtained from the experiments under the GNSS outage of 50 s in a straight line and 30 s in a turning line, respectively.
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Zeng, Y., G. W. Lan, Z. G. Pan, Y. L. Du, and H. L. Shi. "A METHOD OF FORESTRY DATA COLLECTION BASED ON ANDROID SYSTEM AND RTK." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 279–83. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-279-2020.
Повний текст джерелаАнотація:
Abstract. In our country, forestry resource is one of the most important resources, which are of great significance to national economic and sustainable development. The operation mode of only using a mobile terminal to acquire data cannot satisfy the requirements of forestry data collection. A common mobile terminal is widely used to collect data with the advantage of convenient and rapid in field, but the error range of its GNSS chip positioning is 5 to 10 meters. Both positioning accuracy and the accuracy of sublot area measurement cannot meet the requirement in forestry data acquisition. Although RTK has the advantage of high- precision positioning, its handhold terminal is specially used for mobile single point positioning, and it cannot be suitable for forestry special data collection, storage and visualization. Therefore, this paper presents the idea of using bluetooth communication technology to wirelessly connect RTK with Android mobile terminal, the high precision coordinate is obtained and used for storage and display on the screen in real-time. Combining ArcGIS for Android development kit with SpatiaLite database, this paper develops a set of high-precision and intelligent forestry data acquisition system based on Android platform to realize real-time data collection and sublot editing. Compare with conventional forestry data collection method, the method combines the mobile terminal of Android system with RTK can improve work efficiency, which also provides a more efficient technical means for forestry data collection.
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Pepe, Massimiliano, Domenica Costantino, Gabriele Vozza, and Vincenzo Saverio Alfio. "Comparison of Two Approaches to GNSS Positioning Using Code Pseudoranges Generated by Smartphone Device." Applied Sciences 11, no. 11 (May 23, 2021): 4787. http://dx.doi.org/10.3390/app11114787.
Повний текст джерелаАнотація:
The release of Android 7.0 has made raw GNSS positioning data available on smartphones and, as a result, this has allowed many experiments to be developed to evaluate the quality of GNSS positioning using mobile devices. This paper investigates the best positioning, using pseudorange measurement in the Differential Global Navigation Satellite System (DGNSS) and Single Point Positioning (SPP), obtained by smartphones. The experimental results show that SPP can be comparable to the DGNSS solution and can generally achieve an accuracy of one meter in planimetric positioning; in some conditions, an accuracy of less than one meter was achieved in the Easting coordinate. As far as altimetric positioning is concerned, it has been demonstrated that DGNSS is largely preferable to SPP. The aim of the research is to introduce a statistical method to evaluate the accuracy and precision of smartphone positioning that can be applied to any device since it is based only on the pseudoranges of the code. In order to improve the accuracy of positioning from mobile devices, two methods (Tukey and K-means) were used and applied, as they can detect and eliminate outliers in the data. Finally, the paper shows a case study on how the implementation of SPP on GIS applications for smartphones could improve citizen science experiments.
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Niu, Zun, Ping Nie, Lin Tao, Junren Sun, and Bocheng Zhu. "RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm for Smartphones." Sensors 19, no. 14 (July 22, 2019): 3228. http://dx.doi.org/10.3390/s19143228.
Повний текст джерелаАнотація:
Real-time kinematic (RTK) technique is widely used in modern society because of its high accuracy and real-time positioning. The appearance of Android P and the application of BCM47755 chipset make it possible to use single-frequency RTK and dual-frequency RTK on smartphones. The Xiaomi Mi 8 is the first dual-frequency Global Navigation Satellite System (GNSS) smartphone equipped with BCM47755 chipset. However, the performance of RTK in urban areas is much poorer compared with its performance under the open sky because the satellite signals can be blocked by the buildings and trees. RTK can't provide the positioning results in some specific areas such as the urban canyons and the crossings under an overpass. This paper combines RTK with an IMU-based pedestrian navigation algorithm. We utilize attitude and heading reference system (AHRS) algorithm and zero velocity update (ZUPT) algorithm based on micro electro mechanical systems (MEMS) inertial measurement unit (IMU) in smartphones to assist RTK for the sake of improving positioning performance in urban areas. Some tests are carried out to verify the performance of RTK on the Xiaomi Mi 8 and we respectively assess the performances of RTK with and without the assistance of an IMU-based pedestrian navigation algorithm in urban areas. Results on actual tests show RTK with the assistance of an IMU-based pedestrian navigation algorithm is more robust and adaptable to complex environments than that without it.
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Li, Wanqing, Jiangbo Song, Junzhi Li, and Xiangwei Zhu. "Improving GNSS positioning performance of Android smart devices by a novel pseudorange correction method." Measurement Science and Technology, January 6, 2023. http://dx.doi.org/10.1088/1361-6501/acb0ed.
Повний текст джерелаАнотація:
Abstract To achieve high-precision GNSS positioning of smart devices, it is necessary to correct the pseudorange measurements, especially to reduce time-related errors. Traditional pseudorange smoothing methods fail to cope with measurement anomalies and do not consider the time correlation. Therefore, a time-correlated pseudorange correction (TCPC) method is proposed. The Mann-Kendall trend test and breakpoint detection are used to address data anomalies and pseudorange errors are estimated with an adaptive smoothing method. Using the proposed method, the GNSS receivers can improve the pseudorange accuracy and availability for better single-point positioning performance. The positioning errors are reduced by 10%−45% for different GNSS receivers. Moreover, the circular antenna motion mechanism is used to reduce the measurement correlation and enhance observability. Using the TCPC method in the smartphone antenna motion case, the improvement rates of positioning accuracies are 43%−76% and the horizontal positioning accuracy is improved from meter level to decimeter level.
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Wang, Liang, Zishen Li, Ningbo Wang, and Zhiyu Wang. "Real-time GNSS precise point positioning for low-cost smart devices." GPS Solutions 25, no. 2 (March 3, 2021). http://dx.doi.org/10.1007/s10291-021-01106-1.
Повний текст джерелаАнотація:
AbstractGlobal Navigation Satellite System raw measurements from Android smart devices make accurate positioning possible with advanced techniques, e.g., precise point positioning (PPP). To achieve the sub-meter-level positioning accuracy with low-cost smart devices, the PPP algorithm developed for geodetic receivers is adapted and an approach named Smart-PPP is proposed in this contribution. In Smart-PPP, the uncombined PPP model is applied for the unified processing of single- and dual-frequency measurements from tracked satellites. The receiver clock terms are parameterized independently for the code and carrier phase measurements of each tracking signal for handling the inconsistency between the code and carrier phases measured by smart devices. The ionospheric pseudo-observations are adopted to provide absolute constraints on the estimation of slant ionospheric delays and to strengthen the uncombined PPP model. A modified stochastic model is employed to weight code and carrier phase measurements by considering the high correlation between the measurement errors and the signal strengths for smart devices. Additionally, an application software based on the Android platform is developed for realizing Smart-PPP in smart devices. The positioning performance of Smart-PPP is validated in both static and kinematic cases. Results show that the positioning errors of Smart-PPP solutions can converge to below 1.0 m within a few minutes in static mode and the converged solutions can achieve an accuracy of about 0.2 m of root mean square (RMS) both for the east, north and up components. For the kinematic test, the RMS values of Smart-PPP positioning errors are 0.65, 0.54 and 1.09 m in the east, north and up components, respectively. Static and kinematic tests both show that the Smart-PPP solutions outperform the internal results provided by the experimental smart devices.
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Li, Xingxing, Hao Wang, Xin Li, Linyang Li, Hongbo Lv, Zhiheng Shen, Chunxi Xia, and Hailong Gou. "PPP rapid ambiguity resolution using Android GNSS raw measurements with a low-cost helical antenna." Journal of Geodesy 96, no. 10 (September 27, 2022). http://dx.doi.org/10.1007/s00190-022-01661-6.
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Tunalioglu, Nursu, Taylan Ocalan, and Ali Hasan Dogan. "Precise Point Positioning with GNSS Raw Measurements from an Android Smartphone in Marine Environment Monitoring." Marine Geodesy, January 25, 2022, 1–21. http://dx.doi.org/10.1080/01490419.2022.2027831.
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Zangenehnejad, Farzaneh, and Yang Gao. "GNSS smartphones positioning: advances, challenges, opportunities, and future perspectives." Satellite Navigation 2, no. 1 (November 16, 2021). http://dx.doi.org/10.1186/s43020-021-00054-y.
Повний текст джерелаАнотація:
AbstractStarting from 2016, the raw Global Navigation Satellite System (GNSS) measurements can be extracted from the Android Nougat (or later) operating systems. Since then, GNSS smartphone positioning has been given much attention. A high number of related publications indicates the importance of the research in this field, as it has been doing in recent years. Due to the cost-effectiveness of the GNSS smartphones, they can be employed in a wide variety of applications such as cadastral surveys, mapping surveying applications, vehicle and pedestrian navigation and etc. However, there are still some challenges regarding the noisy smartphone GNSS observations, the environment effect and smartphone holding modes and the algorithm development part which restrict the users to achieve high-precision smartphone positioning. In this review paper, we overview the research works carried out in this field with a focus on the following aspects: first, to provide a review of fundamental work on raw smartphone observations and quality assessment of GNSS observations from major smart devices including Google Pixel 4, Google Pixel 5, Xiaomi Mi 8 and Samsung Ultra S20 in terms of their signal strengths and carrier-phase continuities, second, to describe the current state of smartphone positioning research field until most recently in 2021 and, last, to summarize major challenges and opportunities in this filed. Finally, the paper is concluded with some remarks as well as future research perspectives.
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Li, Zishen, Liang Wang, Ningbo Wang, Ran Li, and Ang Liu. "Real-time GNSS precise point positioning with smartphones for vehicle navigation." Satellite Navigation 3, no. 1 (August 22, 2022). http://dx.doi.org/10.1186/s43020-022-00079-x.
Повний текст джерелаАнотація:
AbstractThe availability of raw Global Navigation Satellite System (GNSS) measurements from Android smart devices gives new possibilities for precise positioning solutions, e.g., Precise Point Positioning (PPP). However, the accuracy of the PPP with smart devices currently is a few meters due to the poor quality of the raw GNSS measurements in a kinematic scenario and in urban environments, particularly when the smart devices are placed inside vehicles. To promote the application of GNSS PPP for land vehicle navigation with smart devices, this contribution studies the real-time PPP with smartphones. For data quality analysis and positioning performance validation, two vehicle-based kinematic positioning tests were carried out using two Huawei Mate30 smartphones and two Huawei P40 smartphones with different installation modes: the vehicle-roof mode with smartphones mounted on the top roof outside the vehicle, and the dashboard mode with smartphones stabilized on the dashboard inside the vehicle. To realize high accuracy positioning, we proposed a real-time smartphone PPP method with the data processing strategies adapted for smart devices. Positioning results show that the real-time PPP can achieve the horizontal positioning accuracy of about 1–1.5 m in terms of root-mean-square and better than 2.5 m at the 95th percentile for the vehicle-based kinematic positioning with the experimental smartphones mounted on the dashboard inside the vehicle, which is the real scenario in vehicle navigation.
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Tomaštík, Julián, Juliána Chudá, Daniel Tunák, František Chudý, and Miroslav Kardoš. "Advances in smartphone positioning in forests: dual-frequency receivers and raw GNSS data." Forestry: An International Journal of Forest Research, September 7, 2020. http://dx.doi.org/10.1093/forestry/cpaa032.
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Abstract Smartphones with their capability to receive Global Navigation Satellite Systems (GNSS) signals can be currently considered the most common devices used for positioning tasks, including forestry applications. This study focuses on possible improvements related to two crucial changes implemented into Android smartphone positioning in the last 3 years – dual-frequency (L1/L5) GNSS receivers and the possibility of recording raw GNSS data. The study comprises three experiments: (1) real-time measurements of individual points, (2) real-time recording of trajectories, and (3) post-processing of raw GNSS data provided by the smartphone receiver. The real-time tests were conducted using final positions provided by the internal receiver, i.e. without further processing or averaging. The test on individual points has proven that the Xiaomi Mi8 smartphone with a multi-constellation, dual-frequency receiver was the only device whose accuracy was not significantly different from single-frequency mapping-grade receiver under any conditions. The horizontal accuracy of most devices was lower during leaf-on season (root mean square errors between 5.41 and 12.55 m) than during leaf-off season (4.10–11.44 m), and the accuracy was significantly better under open-area conditions (1.72–4.51 m) for all tested devices when compared with forest conditions. Results of the second experiment with track recording suggest that smartphone receivers are better suited for dynamic applications – the mean shift between reference and measured trajectories varied from 1.23 to 5.98 m under leaf-on conditions. Post-processing of the raw GNSS data in the third experiment brought very variable results. We achieved centimetre-level accuracy under open-area conditions; however, in forest, the accuracies varied from meters to tens of meters. Observed loss of the signal strength in the forest represented ~20 per cent of the open-area value. Overall, the multi-constellation, dual-frequency receiver provided more robust and accurate positional solutions compared with single-frequency smartphones. Applicability of the raw GNSS data must be further studied especially in forests, as the provided data are highly susceptible to multipath and other GNSS adverse effects.
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Darugna, Francesco, Jannes B. Wübbena, Gerhard Wübbena, Martin Schmitz, Steffen Schön, and André Warneke. "Impact of robot antenna calibration on dual-frequency smartphone-based high-accuracy positioning: a case study using the Huawei Mate20X." GPS Solutions 25, no. 1 (November 10, 2020). http://dx.doi.org/10.1007/s10291-020-01048-0.
Повний текст джерелаАнотація:
Abstract The access to Android-based Global Navigation Satellite Systems (GNSS) raw measurements has become a strong motivation to investigate the feasibility of smartphone-based positioning. Since the beginning of this research, the smartphone GNSS antenna has been recognized as one of the main limitations. Besides multipath (MP), the radiation pattern of the antenna is the main site-dependent error source of GNSS observations. An absolute antenna calibration has been performed for the dual-frequency Huawei Mate20X. Antenna phase center offset (PCO) and variations (PCV) have been estimated to correct for antenna impact on the L1 and L5 phase observations. Accordingly, we show the relevance of considering the individual PCO and PCV for the two frequencies. The PCV patterns indicate absolute values up to 2 cm and 4 cm for L1 and L5, respectively. The impact of antenna corrections has been assessed in different multipath environments using a high-accuracy positioning algorithm employing an undifferenced observation model and applying ambiguity resolution. Successful ambiguity resolution is shown for a smartphone placed in a low multipath environment on the ground of a soccer field. For a rooftop open-sky test case with large multipath, ambiguity resolution was successful in 19 out of 35 data sets. Overall, the antenna calibration is demonstrated being an asset for smartphone-based positioning with ambiguity resolution, showing cm-level 2D root mean square error (RMSE).
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Li, Guangcai, and Jianghui Geng. "Characteristics of raw multi-GNSS measurement error from Google Android smart devices." GPS Solutions 23, no. 3 (July 2019). http://dx.doi.org/10.1007/s10291-019-0885-4.
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