On the Mitigation of Ionospheric Scintillation in Advanced GNSS Receivers

Vilà‐Valls, Jordi; Closas, Pau; Fernández‐Prades, Carles; Curran, James T.

doi:10.1109/taes.2018.2798480

Cited by 24 publications

(18 citation statements)

References 31 publications

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“…An expression can be obtained using a fourth‐order Taylor approximation of the time‐varying phase evolution as

θ_{d, k} = θ_{0} + 2 π false(f_{d, k} k T_{s} + \underset{Doppler dynamics}{\underset{︸}{f_{r, k} k^{2} T_{s}^{2} / 2 + f_{j, k} k^{3} T_{s}^{3} / 6}} false),

where

θ_{0}

(rad) is a random constant phase value,

f_{d, k}

(Hz) the carrier Doppler frequency shift,

f_{r, k}

(Hz/s) the Doppler frequency rate, and

f_{j, k}

(Hz/s

^{2}

) the Doppler acceleration. Notice that the phase can be expressed recursively as

(\begin{matrix} θ_{d, k} \\ f_{d, k} \\ f_{r, k} \\ f_{j, k} \end{matrix}) = (\begin{matrix} 1 & 2 π T_{I} & π T_{I}^{2} & π T_{I}^{3} / 3 \\ 0 & 1 & T_{I} & T_{I}^{2} / 2 \\ 0 & 0 & 1 & T_{I} \\ 0 & 0 & 0 & 1 \end{matrix}) (\begin{matrix} θ_{d, k - 1} \\ f_{d, k - 1} \\ f_{r, k - 1} \\ f_{j, k - 1} \end{matrix}),

which is very convenient for state‐space formulations of the tracking problem (Vilà‐Valls, Closas, Fernández‐Prades, & Curran, 2018). Lower o...…”

Section: Background On Gnss and Ionospheric Scintillationmentioning

confidence: 99%

“…(2013) and later extended in Vilà‐Valls, Closas, and Fernández‐Prades (2015b) and Vilà‐Valls et al. (2015a, 2018), where the scintillation physical phenomena is mathematically modeled as an autoregressive (AR) process, and embedded into the state‐space formulation, which allows decoupling both phase contributions of interest. This method has been tested using both synthetic and real data, providing very promising performance results.…”

Section: Scintillation Mitigationmentioning

confidence: 99%

“…Recently, to increase robustness in time‐varying scenarios, adaptive AR parameter estimation techniques have been considered in Vilà‐Valls, Fernández‐Prades, Closas, and Arribas (2017) and Fohlmeister et al. (2018), showing that the approach in Vilà‐Valls, Closas, Fernández‐Prades, and Curran (2018) is the latest trend on single‐frequency scintillation mitigation, and must be seen as the performance benchmark for the derivation of new scintillation mitigation strategies.…”

Section: Scintillation Mitigationmentioning

confidence: 99%

“…The AR‐based scintillation modeling within a KF‐like architecture in Vilà‐Valls, Closas, Fernández‐Prades, and Curran (2018) was extended to the multi‐frequency case in Vilà‐Valls, Closas, and Curran (2017b) and Vilà‐Valls, Closas, and Curran (2017a), using a multi‐frequency state‐space model formulation and a multivariate AR scintillation model. It was shown that exploiting both multi‐frequency measurements and the scintillation approximation embedded into the filter improves the overall performance and robustness.…”

Section: Scintillation Mitigationmentioning

confidence: 99%

“…From a signal‐processing perspective (Closas, Luise, Avila‐Rodriguez, Hegarty, & Lee, 2017), ionospheric scintillation detection, monitoring, and mitigation are certainly some of the more challenging and appealing GNSS scenarios. Because ionospheric scintillation is not related to the local environment, as in the case of multipath or shadowing, it can degrade receiver performance even under ideal open‐sky conditions (Vilà‐Valls, Closas, Fernández‐Prades, & Curran, 2018). As it will be later explained in detail, ionospheric scintillation may induce fast phase variations (i.e., phase scintillation) and deep amplitude fades (i.e., amplitude scintillation), effects which are frequency and latitude dependent.…”

Section: Introductionmentioning

confidence: 99%

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Survey on signal processing for GNSS under ionospheric scintillation: Detection, monitoring, and mitigation

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Ionospheric scintillation is the physical phenomena affecting radio waves coming from the space through the ionosphere. Such disturbance is caused by ionospheric electron‐density irregularities and is a major threat in Global Navigation Satellite Systems (GNSS). From a signal‐processing perspective, scintillation is one of the most challenging propagation scenarios, particularly affecting high‐precision GNSS receivers and safety critical applications where accuracy, availability, continuity, and integrity are mandatory. Under scintillation, GNSS signals are affected by amplitude and phase variations, which mainly compromise the synchronization stage of the receiver. To counteract these effects, one must resort to advanced signal‐processing techniques such as adaptive/robust methods, machine learning, or parameter estimation. This contribution reviews the signal‐processing landscape in GNSS receivers, with emphasis on different detection, monitoring, and mitigation problems. New results using real data are provided to support the discussion. To conclude, future perspectives of interest to the GNSS community are discussed.

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“…An expression can be obtained using a fourth‐order Taylor approximation of the time‐varying phase evolution as