A statistical characterization of the Galileo-to-GPS inter-system bias

Gioia, Ciro; Borio, Daniele

doi:10.1007/s00190-016-0925-6

Cited by 32 publications

(23 citation statements)

References 15 publications

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“…A receiver-dependent bias is clearly visible in Figure 8, as previously noted by [17,20,21]. In order to better investigate this receiver dependent bias, the time series relative to the mean values of Septentrio stations have been differentiated.…”

Section: Differential Time Offsetsupporting

confidence: 52%

Investigation on Reference Frames and Time Systems in Multi-GNSS

Nicolini

2018

Remote Sensing

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Receivers able to track satellites belonging to different GNSSs (Global Navigation Satellite Systems) are available on the market. To compute coordinates and velocities it is necessary to identify all the elements that contribute to interoperability of the different GNSSs. For example the timescales kept by different GNSSs have to be aligned. Receiver-specific biases, or firmware-dependent biases, need to be calibrated. The reference frame used in the representation of the orbits must be unique. In this paper we address the interoperability issues from the standpoint of a Single Point Positioning (SPP) user, i.e., using pseudoranges and broadcast ephemeris. The biases between GNSSs timescales and receiver-dependent biases are analyzed for a set of 31 MGEX (Multi-GNSS Experiment) stations over a time span of more than three years.

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Section: Differential Time Offsetsupporting

confidence: 52%

Investigation on Reference Frames and Time Systems in Multi-GNSS

Nicolini

2018

Remote Sensing

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“…Since there exists inter-system bias (ISB) between different constellations, the range measurement will contain an additional error that degrades the GNSS positioning accuracy [17,18]. The ISB estimated using mass market receivers are noisier than obtained using geodetic receivers [19]. Generally, the ISBs are parameterized and estimated in the positioning equation.…”

Section: Gnss Position and Velocity Estimationmentioning

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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“…In order to provide a remedy for the shortcomings of the single GNSS performance, a possible approach is the use of multi-constellation. The methods of the determination of the intersystem time offsets, which is characterized by a relative bias and drift between GPS and Galileo time scales, are proposed [ 6 , 7 ]. In particular, integrity monitoring has a great importance in safety critical operation, and the research for the performance assessment of the capabilities of integrity algorithms defined as receiver autonomous integrity monitoring (RAIM) are proposed [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Map-Matching Algorithm for Real-Time Position Accuracy Improvement with a Low-Cost GPS Receiver

Kang

Kim

Park

et al. 2018

Sensors

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This paper proposes a real-time position accuracy improvement method for a low-cost global positioning system (GPS), which uses geographic data for forming a digital road database in the digital map information. We link the vehicle’s location to the position on the digital map using the map-matching algorithm to improve the position accuracy. In the proposed method, we can distinguish the vehicle direction on the road and enhance the horizontal accuracy using the geographic data composed of the vector point set of the digital map. We use the iterative closest point (ICP) algorithm that calculates the rotation matrix and the translation vector to compensate for the disparity between the GPS and the digital map information. We also use the least squares method to correct the error caused by the rotation of the ICP algorithm and link on the digital map to eliminate the residual disparity. Finally, we implement the proposed method in real time with a low-cost embedded system and demonstrate the effectiveness of the proposed method through various experiments.

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A statistical characterization of the Galileo-to-GPS inter-system bias

Cited by 32 publications

References 15 publications

Investigation on Reference Frames and Time Systems in Multi-GNSS

Investigation on Reference Frames and Time Systems in Multi-GNSS

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

An Enhanced Map-Matching Algorithm for Real-Time Position Accuracy Improvement with a Low-Cost GPS Receiver

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