Estimation of GPS Differential Code Biases Based on Independent Reference Station and Recursive Filter

Yuan, Liangliang; Jin, Shuanggen; Hoque, Mohammed Mainul

doi:10.3390/rs12060951

Cited by 7 publications

(4 citation statements)

References 41 publications

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“…They are applicable in many scientific and engineering applications. Other research work confirms that DCB can also be estimated using a recursive method together with the selection of an individual reference station [10]. Among presented techniques, the DGNSS technique based on pseudorange correction (PRC) is also approved in number of applications improving real-time positioning accuracy in low-cost satellite receivers.…”

Section: Dgps (Differential Globalmentioning

confidence: 96%

Performance of DGPS Smartphone Positioning with the Use of P(L1) vs. P(L5) Pseudorange Measurements

2022

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This paper presents numerical analyzes of code differential GPS positioning with the use of two Huawei P30 Pro mobile phones. Code observations on L1 and L5 frequencies were chosen for DGPS positioning analysis. For project purposes, we additionally used one high-class geodetic GNSS receiver (Javad Alpha) acting as a reference station. Smartphones were placed at the same distance of 0.5 m from the reference receiver. Such a close distance was specially planned by the authors in order to achieve identical observation conditions. Thus, it was possible to compare the DGPS positioning accuracy using the same satellites and the P(L1) and P(L5) code only, for single observation epochs and for sequential DGPS adjustment. Additionally, the precision of observations of the second differences in the observations P(L1) and P(L5) was analyzed. In general, the use of the P(L5) code to derive DGPS positions has made it possible to significantly increase the accuracy with respect to the positions derived using the P(L1) code. Average errors of horizontal and vertical coordinates were about 60–80% lower for the DGPS solution using the P(L5) code than using the P(L1) code. Based on the simulated statistical analyses, an accuracy of about 0.4 m (3D) with 16 satellites may be obtained using a smartphone with P(L5) code. An accuracy of about 0.3 m (3D) can be achieved with 26 satellites.

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Section: Dgps (Differential Globalmentioning

confidence: 96%

Performance of DGPS Smartphone Positioning with the Use of P(L1) vs. P(L5) Pseudorange Measurements

2022

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“…The introduction of this assumption helps mitigating the overestimation or underestimation of electron density at the intersected gridpoints because of the introduction of non‐zero off‐diagonal terms. In this study, the satellite and receiver DCB errors are set to 0.1 and 1TECU, respectively (Yuan et al., 2020a, 2020b, 2021a). And for simplicity, the first term of the right hand side of Equation is assumed to be 0, which assumes a perfect VTEC modeling in the TEC calibration procedure.…”

Section: The Nedam Approach Using 4d‐var Assimilationmentioning

confidence: 99%

The Four‐Dimensional Variational Neustrelitz Electron Density Assimilation Model: NEDAM

2023

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It is of particular interest to retrieve the electron density distribution from Global Navigation Satellite System (GNSS) observations due to its high density and broad spatial-temporal coverage. There are several commonly used methods such as Computerized Ionospheric Tomography (CIT), Bayesian analysis and Kalman filter (Bust et al., 2001;Prol et al., 2021;Scherliess et al., 2004). However, different methods differ in many aspects including computational cost, data storage and management cost, compatibility for different kinds of observations and flexibility in practical applications.Several different techniques are developed to perform posterior analyses of the ionospheric electron density. One of the traditional techniques is CIT. CIT is a direct inversion technique that develops a two-dimensional electron density specification from a series of one-dimensional ionospheric observations and various minimization criteria (Kronschnabl et al., 1997;Raymund, 1995;Raymund et al., 1994). Similar work by other groups have led to the development of more advanced spatial analysis techniques. Howe et al. (1998) developed a Kalman filter method for ionospheric reconstruction based on spherical harmonics and Empirical Orthogonal Function describing the horizontal and vertical distribution, respectively. Manuel Hernández-Pajares et al. (1999, 2002)

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“…Two different categories of methods are available for determining DCBs based on data from ground GNSS receiver stations. The first approach is to determine satellite and receiver DCBs simultaneously through local or global ionospheric TEC modeling [ 7 , 8 , 9 ]. Therefore, the errors in ionospheric model coefficients will lead to errors in the satellite and receiver DCB estimates using this method.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Analysis of GNSS Differential Code Biases (DCBs) Using a Multi-Spacing Software Receiver

Wang

Zhao

Gao

2021

Sensors

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In the use of global navigation satellite systems (GNSS) to monitor ionosphere variations by estimating total electron content (TEC), differential code biases (DCBs) in GNSS measurements are a primary source of errors. Satellite DCBs are currently estimated and broadcast to users by International GNSS Service (IGS) using a network of GNSS hardware receivers which are inside structure fixed. We propose an approach for satellite DCB estimation using a multi-spacing GNSS software receiver to analyze the influence of the correlator spacing on satellite DCB estimates and estimate satellite DCBs based on different correlator spacing observations from the software receiver. This software receiver-based approach is called multi-spacing DCB (MSDCB) estimation. In the software receiver approach, GNSS observations with different correlator spacings from intermediate frequency datasets can be generated. Since each correlator spacing allows the software receiver to output observations like a local GNSS receiver station, GNSS observations from different correlator spacings constitute a network of GNSS receivers, which makes it possible to use a single software receiver to estimate satellite DCBs. By comparing the MSDCBs to the IGS DCB products, the results show that the proposed correlator spacing flexible software receiver is able to predict satellite DCBs with increased flexibility and cost-effectiveness than the current hardware receiver-based DCB estimation approach.

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Estimation of GPS Differential Code Biases Based on Independent Reference Station and Recursive Filter

Cited by 7 publications

References 41 publications

Performance of DGPS Smartphone Positioning with the Use of P(L1) vs. P(L5) Pseudorange Measurements

Performance of DGPS Smartphone Positioning with the Use of P(L1) vs. P(L5) Pseudorange Measurements

The Four‐Dimensional Variational Neustrelitz Electron Density Assimilation Model: NEDAM

Estimation and Analysis of GNSS Differential Code Biases (DCBs) Using a Multi-Spacing Software Receiver

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