A New DGNSS Positioning Infrastructure for Android Smartphones

Weng, Duojie; Gan, Xingli; Chen, Wu; Ji, Shengyue; Lü, Yuanyuan

doi:10.3390/s20020487

Cited by 18 publications

(10 citation statements)

References 15 publications

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“…The results indicated that the carrier phase-based static differential positioning using GPS and Galileo provide centimeter-and decimeter-level precision in the horizontal and vertical components, respectively, over a short baseline. Weng et al (2020) described the derivation of the DGNSS based on the NMEA messages. They then proposed the DGNSS infrastructures that correct the standalone GNSS position of smartphones using the corrections from the reference station.…”

Section: Relative Positioningmentioning

confidence: 99%

GNSS smartphones positioning: advances, challenges, opportunities, and future perspectives

2021

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Starting 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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Section: Relative Positioningmentioning

confidence: 99%

GNSS smartphones positioning: advances, challenges, opportunities, and future perspectives

2021

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“…Dual frequency GNSS data from five reference stations were firstly post processed with a network RTK software. In the software, the double-differenced ionospheric delay was calculated after ambiguities on two frequencies had been resolved, as shown in Equation (1). With the known locations of reference stations, the double-differenced ionospheric delay was modelled using a linear function of coordinate differences in the horizontal plane between master and auxiliary reference stations, as shown in Equation (2).…”

Section: Evaluation Of Modified Dgnssmentioning

confidence: 99%

“…Since the DGNSS is easy to use, it is supported by most low-cost GNSS receivers. Today, raw measurements are available from smartphones, and DGNSS is a basic technique that improves the accuracy of smartphone positioning [1]. The accuracy of DGNSS is mainly affected by uncommon errors and decorrelation errors.…”

Section: Introductionmentioning

confidence: 99%

Improving DGNSS Performance through the Use of Network RTK Corrections

Weng

Lü

et al. 2021

Remote Sensing

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The differential global navigation satellite system (DGNSS) is an enhancement system that is widely used to improve the accuracy of single-frequency receivers. However, distance-dependent errors are not considered in conventional DGNSS, and DGNSS accuracy decreases when baseline length increases. In network real-time kinematic (RTK) positioning, distance-dependent errors are accurately modelled to enable ambiguity resolution on the user side, and standard Radio Technical Commission for Maritime Services (RTCM) formats have also been developed to describe the spatial characteristics of distance-dependent errors. However, the network RTK service was mainly developed for carrier-phase measurements on professional user receivers. The purpose of this study was to modify the local-area DGNSS through the use of network RTK corrections. Distance-dependent errors can be reduced, and accuracy for a longer baseline length can be improved. The results in the low-latitude areas showed that the accuracy of the modified DGNSS could be improved by more than 50% for a 17.9 km baseline during solar active years. The method in this paper extends the use of available network RTK corrections with high accuracy to normal local-area DGNSS applications.

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“…Li and Geng (16) showed that GNSS signals generated by smart devices have nonuniform signal strength, rapid CNo changes, and low CNo values at high elevations and that the pseudorange noise was 10 times greater than that of a survey receiver. Weng et al (17) proposed a differential GNSS infrastructure that independently calibrates the GNSS position of a smartphone using a reference station calibration. Guo et al (18) confirmed that the CNo of smart devices was 4-11 dB-Hz lower than that of surveying receivers through the analysis of stationary and moving positions in open space in studies on the dualfrequency GNSS observation data characteristics of the Xiaomi Mi8 in an urban environment.…”

Section: Introductionmentioning

confidence: 99%

Improving Positional Accuracy Using Relative Measurement between Android Smartphones

Jang¹,

Kim²,

Jung³

et al. 2022

Sensors and Materials

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In this study, we propose a relative and clustering analysis correction (RCC) technique capable of improving the location accuracy of a smartphone global navigation satellite system (GNSS). The RCC technique improves the accuracy of the Android GNSS by eliminating common error components from pseudoscope measurements as well as noncommon errors through cluster analysis using Android GNSS signal attributes. Cluster analysis was applied to the RCC technique using the optimal clustering method among the hierarchical clustering, K-means clustering, and neural network clustering methods. As a result of verifying the RCC technique, the following results were obtained. The distance error of a zero-baseline experiment, which was performed to check the relative accuracy and precision between smartphone GNSSs, was 0.572 m for two sessions, which showed that the noise-causing error of the Android smartphone GNSS used in the experiment occurred similarly in each session. Positioning accuracy was much lower in a multipath environment than in an open environment due to the reflection and refraction of satellite signals by obstacles, such as buildings around the receiver and multipath generation due to low-elevation non-line-of-sight satellite signals. However, observations confirmed that applying the RCC technology to the Android smartphone GNSS with errors of more than 5 m in multipath environments can secure high location accuracy, even in multipath environments.Recent smartphones have been equipped with GNSS chipsets, and location information can be obtained through an application programming interface (API). (1)(2)(3) In May 2016, Google announced that it would provide GNSS raw data for smartphones and tablets that support the Android 7.0 (Android Nougat) operating system. Smartphones equipped with GNSS chipsets running Android 7.0 or later can calculate not only a user's location information (latitude, longitude, and elevation) but also the pseudodistance between the satellite and receiver as well as provide a direct signal timestamp that allows the user's location to be calculated. Notably, GNSS raw data are now available. (4)(5)(6) In particular, the first dual-frequency GNSS chipset was released in June 2018 in a smartphone equipped with a BCM47755 location hub, which increased the availability of satellite signals and improved positioning accuracy. The provision of such GNSS raw data enabled the design of an advanced positioning algorithm and improved accuracy in a usercentered location-based service (LBS). (7,8) Currently, programs and applications that can use GNSS raw data are being developed, and various studies related to the high positioning accuracy of smartphones are being conducted. For high-precision geodetic applications, precise point positioning (PPP) is well known to provide high positioning accuracy as evidenced in various research contributions. (6) The use of the dualfrequency ionosphere free linear combination has typically defined conventional GNSS PPP processing. (8) However, owing to satellite mod...

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A New DGNSS Positioning Infrastructure for Android Smartphones

Cited by 18 publications

References 15 publications

GNSS smartphones positioning: advances, challenges, opportunities, and future perspectives

GNSS smartphones positioning: advances, challenges, opportunities, and future perspectives

Improving DGNSS Performance through the Use of Network RTK Corrections

Improving Positional Accuracy Using Relative Measurement between Android Smartphones

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