On the feasibility of cm-accurate positioning via a smartphone's antenna and GNSS chip

Humphreys, Todd E.; Murrian, Matthew J.; Diggelen, Frank van; Podshivalov, Sergei; Pesyna, Kenneth M.

doi:10.1109/plans.2016.7479707

Cited by 72 publications

(61 citation statements)

References 9 publications

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“…They showed that smartphone measurements were noisy and suffered from a significant number of outliers in comparison with those collected by a U-Blox low-cost receiver and achieved only a meter-level positioning. In 2016, Humphreys et al [3] were able to access GNSS raw measurements collected by a modified Samsung Galaxy S5 smartphone equipped with a Broadcom GNSS chipset running a customized Android Marshmallow (provided by Broadcom) including a modified GPS library that recorded RINEX (Receiver Independent Exchange Format) files to the phone's storage card. Their analysis revealed that under typical smartphone use, the effects of local multipath was the main drawback to achieving a centimeter-accurate smartphone positioning.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Dual Frequency GNSS Observations from a Xiaomi Mi 8 Android Smartphone and Positioning Performance Analysis

2019

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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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Section: Introductionmentioning

confidence: 99%

Assessment of Dual Frequency GNSS Observations from a Xiaomi Mi 8 Android Smartphone and Positioning Performance Analysis

2019

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“…Thus, with the advent of the Android Nougat operating system (version 7.x or 8.x), some smart devices allow direct access to raw data and the PVT (position velocity time) solution by acquiring pseudo ranges and carrier-phase from the chipset inside [5,6]. Many other sensors are available today on smartphones; most of them are related to internal applications (e.g., proximity sensor, light sensors), while others (e.g., inertial measurements unit and camera) can be used for estimating a positioning solution, but these aspects are out of the scope of this paper.…”

Section: Introductionmentioning

confidence: 99%

GNSS Positioning Using Mobile Devices with the Android Operating System

Dabove

Pietra

Piras

2020

IJGI

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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.

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“…Under ideal scenarios, consumer-grade GNSS receivers can reach several meters of positioning accuracy, the improvement of which will further promote the diversification of LBS [1]. However, the positioning capability will decrease in some harsh conditions, such as urban canyons or short distance tunnels, where GNSS signals suffer from a low signal-to-noise ratio and severe multipath [2,3]. Multi-GNSS receiver autonomous integrity monitoring (RAIM) can reduce the large positioning errors and achieve satisfactory positioning performance in degraded-signal The phase and Doppler observations are provided directly, while the pseudorange is computed with the time of signal transmitted and received as follows:…”

Section: Introductionmentioning

confidence: 99%

Walker: Continuous and Precise Navigation by Fusing GNSS and MEMS in Smartphone Chipsets for Pedestrians

et al. 2019

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The continual miniaturization of mass-market sensors built in mobile intelligent terminals has inspired the development of accurate and continuous navigation solution for portable devices. With the release of Global Navigation Satellite System (GNSS) observations from the Android Nougat system, smartphones can provide pseudorange, Doppler, and carrier phase observations of GNSS. However, it is still a challenge to achieve the seamless positioning of consumer applications, especially in environments where GNSS signals suffer from a low signal-to-noise ratio and severe multipath. This paper introduces a dedicated android smartphone application called Walker that integrates the GNSS navigation solution and MEMS (micro-electromechanical systems) sensors to enable continuous and precise pedestrian navigation. Firstly, we introduce the generation of GNSS and MEMS observations, in addition to the architecture of Walker application. Then the core algorithm in Walker is given, including the time-differenced carrier phase improved GNSS single-point positioning and the integration of GNSS and Pedestrian Dead Reckoning (PDR). Finally, the Walker application is tested and the observations of GNSS and MEMS are assessed. The static experiment shows that, with GNSS observations, the RMS (root mean square) values of east, north, and up positioning error are 0.49 m, 0.37 m, and 1.01 m, respectively. Furthermore, the kinematic experiment verifies that the proposed method is capable of obtaining accuracy within 1-3 m for smooth and continuous navigation. scenarios [4,5]. Furthermore, pedestrian navigation systems (PNSs) are expected to provide continuous positioning and enhanced navigation performance under such cases. Based on fusing measurements of IMUs and magnetometers, the characteristics of human gait can be exploited in pedestrian dead reckoning (PDR) algorithms, which are the primary component of PNSs to continuous relative position [6,7]. A PDR system performs three main tasks: step detection, step length estimation, and heading determination [8,9]. However, the performance of a PDR system would significantly decrease with the increase in recursion time, due to sensor noise and model errors. Therefore, the absolute positioning accuracy of GNSS and the relative positioning accuracy of PDR complement each other in a smartphone, enabling the smartphone to provide continuous positioning services.With the release of GNSS observations from the Android Nougat system, smartphones, such as Huawei P10 and Samsung S8 models, can provide pseudorange, Doppler, and carrier phase observations of GNSS systems [10]. Moreover, a new generation of mass-market chips based on dual frequency measurements is next to be commercialized [11]. These released data enable us to develop advanced algorithms to increase positioning accuracy. Using a single-frequency PPP model and an extern SBAS correction, sub-meter accuracy can be reached with android GNSS observations [12]. The decimeter positioning accuracy can also be obtained through rapid-static sur...

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On the feasibility of cm-accurate positioning via a smartphone's antenna and GNSS chip

Cited by 72 publications

References 9 publications

Assessment of Dual Frequency GNSS Observations from a Xiaomi Mi 8 Android Smartphone and Positioning Performance Analysis

Assessment of Dual Frequency GNSS Observations from a Xiaomi Mi 8 Android Smartphone and Positioning Performance Analysis

GNSS Positioning Using Mobile Devices with the Android Operating System

Walker: Continuous and Precise Navigation by Fusing GNSS and MEMS in Smartphone Chipsets for Pedestrians

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