Code-phase based combined GPS-Galileo positioning using Ionosphere-free linear combination

Srinivas, V.; Yedukondalu, K.

doi:10.23919/ursiap-rasc.2019.8738477

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…In Eqs. (6), v represents the residual vector, l denotes the known parameters, and the parameters to be estimated include the changes in receiver coordinates R for both before and after epochs, the variation in tropospheric zenith wet delay component T w , and the receiver clock bias change �δt k . When a sufficient number of visible satellites is available, these parameters can be estimated through the least-squares method or Kalman filtering.…”

Section: Czs-ppp Principlementioning

confidence: 99%

“…The lengthy convergence time of PPP has posed an international challenge, impeding its adoption in the scientific, academic, and industrial communities and preventing its real-time and commercial use. To address the issue of shortening PPP convergence time effectively, international scholars have proposed several classic PPP models, including the Single-Difference (SD) model 1,2 , the Uncombined (UC) positioning model 3 , the University of Calgary model (UoFC) 4 , and the ionosphere-free combination model (IF) [5][6][7][8] . In recent years, with the development of BeiDou Navigation Satellite System (BDS) and Galileo, research on PPP using multi-frequency, multi-system, and multi-frequency multi-system combinations has become a hot topic.BDS-3 was the first to launch onboard real-time PPP services internationally.…”

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confidence: 99%

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A static precise single-point positioning method based on carrier phase zero-baseline self-differencing

Lv,

Cai,

Cai

et al. 2024

Sci Rep

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Satellite navigation positioning has become an indispensable component of everyday life, where precise pinpointing and rapid convergence are crucial in delivering timely and accurate location information. However, due to the damping of integer ambiguities and system residual errors, the rapid convergence of Precise Point Positioning (PPP) implementation is a significant challenge. To address this, this paper proposes a novel Carrier Phase Zero-Baseline Self-Differencing Precise Point Positioning (CZS-PPP) technique and its ionosphere-free fusion model. By employing the proposed CZS-PPP approach in separate scenarios involving BDS-3, GPS, and dual-system settings, we systematically validate the efficacy of the method. The experimental results indicate that the convergence time of the method is less than 4 min in a single-system scenario. Furthermore, in a dual-system scenario, the method can achieve rapid convergence in less than 3 min. The CZS-PPP technique presented demonstrates the elimination of integer ambiguities and the effective suppression of system residuals, in comparison to the conventional method. The proposed approach has demonstrated remarkable performance across different systems, offering a promising new pathway for achieving PPP fast convergence in BDS/GNSS.

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Section: Czs-ppp Principlementioning

confidence: 99%

mentioning

confidence: 99%

A static precise single-point positioning method based on carrier phase zero-baseline self-differencing

Lv,

Cai,

Cai

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

A machine-learning approach to estimate satellite-based position errors

Ramavath,

Perumalla

2023

Journal of Applied Geodesy

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Satellite-based navigation systems are widely used in transportation. GNSS signal’s strength or quality can easily be degraded by local environments. As a result, the position accuracy of satellite-based navigation systems decreases. In this paper, a novel approach for estimating the positioning error is proposed using ML/DL technique. For learning the relationship between position errors and increased data from GNSS receivers without any prior experience, neural networks have become the machine learning option of choice in the past few years. Signal degradation is best measured by dilution of precision, elevation angles, and carrier-to-noise ratios. To estimate the position error of satellite-based navigation systems, neural networks are trained in this paper. This paper applies a long-short-term memory (LSTM) network to model the temporal correlation of position error measurements. Therefore, neural networks are capable of learning the trend of position errors through training.

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Code-phase based combined GPS-Galileo positioning using Ionosphere-free linear combination

Cited by 2 publications

References 4 publications

A static precise single-point positioning method based on carrier phase zero-baseline self-differencing

A static precise single-point positioning method based on carrier phase zero-baseline self-differencing

A machine-learning approach to estimate satellite-based position errors

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