Point Positioning using Extended Kalman Filtering in Single Frequency Beidou Receivers

Farooq, Salma Zainab; Hong, Xuebao; Yang, Dongkai; Xia, Ming; Ada, Echoda Ngbede Joshua

doi:10.1109/upinlbs.2018.8559910

Cited by 1 publication

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For each signal and satellite block of the GPS, GLONASS, Galileo, and BeiDou systems, they established independent empirical stochastic models, considering crosscorrelation and temporal correlation among observation values. However, some errors do not behave as expected, and Farooq et al [23] attempted to apply the extended Kalman flter (EKF) model to single-frequency pseudorange measurements. To accurately describe the user's movement, Guo et al [24] utilized pseudorange and high-precision carrier phase observations to construct the state equation.…”

Section: Related Workmentioning

confidence: 99%

T-SPP: Improving GNSS Single-Point Positioning Performance Using Transformer-Based Correction

Wu,

Wei,

Luo

et al. 2024

International Journal of Intelligent Systems

View full text Add to dashboard Cite

GNSS (global navigation satellite systems) technology enables high-precision single-point positioning (SPP) in open environments. However, the accuracy of GNSS positioning is significantly compromised in complex urban canyons due to signal obstructions and non-line-of-sight propagation errors. To address this challenge, we propose a GNSS displacement estimation algorithm. This method learns nonlinear dependencies between GNSS raw measurements and corresponding position changes, capturing dynamic and layered features in GNSS measurement data for displacement estimation. We introduce a denoising auto-encoder (DAE) to preprocess raw GNSS observations, reducing the impact of noise. The model simultaneously outputs estimated displacement and model confidence. The fusion process dynamically combines positioning results from the SPP algorithm and the D-Tran model, adaptively blending them to achieve accurate and optimal positioning estimation. This approach optimizes the accuracy of estimated positioning results while maintaining confidence in the estimation. Experimental results show a 61% reduction in root mean square error (RMSE) and 100% availability in urban canyon environments compared to traditional single-point positioning techniques.

show abstract

Section: Related Workmentioning

confidence: 99%