Ionospheric delay significantly affects the accuracy of positioning applications, posing a challenge due to the nonhomogeneous electron densities and magnetic fields that characterize the global ionosphere. To address this issue, researchers have recently introduced ionospheric correction models aimed at effectively mitigating ionospheric delay. One such model is NeQuick, which offers a comprehensive 3-D representation of electron density over time, as well as the longitudes, latitudes, and heights of both the satellite transmitter and ground receiver. NeQuick-G relies on three ionospheric coefficients, which are transmitted by Galileo Satellites. These coefficients are optimized for all Galileo sensor stations worldwide, making them less than optimal for local users. Moreover, the coefficient updates are not immediate. To address these limitations, an unscented Kalman filter (UKF) for tracking the three ionospheric coefficients is proposed in this paper. This is achieved by utilizing four local reference emitters and one Low Earth Orbit (LEO) satellite, with the objective of passively geolocating ground-based electromagnetic interference (EMI) sources. The accurate and real-time estimation of the ionosphere provided by the UKF will significantly enhance geolocation accuracy. In the design, the satellite and four ground reference emitters, strategically deployed around the estimated EMI position, are used to measure the ionosphere. The UKF tracks the ionospheric coefficients, updating them in real time and optimizing them specifically for the local region where the EMI is located. These precise values are then employed in the NeQuick-G model to estimate the ionosphere along the path from the EMI source to the individual satellite. Numerical results validate the effectiveness of the proposed approach, combining UKF-enabled NeQuick-G for ionosphere estimation and the subsequent enhancement of single satellite geolocation (SSG) accuracy.


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    Title :

    On Unscented Kalman Filter for NeQuick-G Based Ionosphere Estimation


    Contributors:
    Shen, Dan (author) / Chen, Genshe (author) / Ding, Yanwu (author) / Daniel, Yimin (author) / Pham, Khanh (author)


    Publication date :

    2024-03-02


    Size :

    3836846 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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