Kalman Filter Based PLL Robust Against Ionospheric Scintillation

Susi, Melania; Andreotti, Marcus; Aquino, Márcio

doi:10.5772/58769

Cited by 9 publications

(10 citation statements)

References 9 publications

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“…To avoid the complexity of the optimal Kalman gain computation (which is obtained from the prediction and estimation noise covariances, together with the system noise covariance matrices), these typically consider a constant gain KF implementation and then lose the optimality of the filter. Because the actual receiver working conditions are unknown to some extent, solutions based on adaptive KF (AKFs) have been studied in Zhang, Morton, and Miller (2010); Won, Eissfeller, Pany, and Winkel (2012); Won (2014); Susi, Aquino, Romero, Dovis, and Andreotti (2014); Susi, Andreotti, and Aquino (2014); and Xu, Morton, Jiao, and Rino (2017), which aim at sequentially adapting the filter parameters. Notice that the correct estimation of both noise covariance matrices is not possible due to identifiability issues (Vilà‐Valls, Closas, & Fernández‐Prades, 2015a), then typically only the measurement noise is adjusted.…”

Section: Scintillation Mitigationmentioning

confidence: 99%

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

et al. 2020

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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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Section: Scintillation Mitigationmentioning

confidence: 99%

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

et al. 2020

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“…The available real-time GNSS data is severely scintillated (0.6<S4<1.04(max)) and the possible ARX orders (1), (2) and (3) is shown to choose the best suitable order. It clearly explains that ARx(3) is suitable for real-time amplitude and phase scintillations so that three coefficients are chosen to transition matrix K i.e., ( 1 p ⇒ 3, 2 p ⇒ 3) (Eqn (8)). The power spectral density with respective to ARX modelling results for both CSM and real-time data sets can be seen from Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The phase dynamics are additional phase variations that occur because of changes in the position of satellite and receiver. The available PLL cannot withstand with the effects on phase by dynamics and scintillations 8 . Prolific research taken place in improving the carrier tracking algorithm to make GNSS receiver self-sufficient to work even in stressful propagation conditions.…”

mentioning

confidence: 99%

“…It involves the process of prediction and correction through a sequence of steps 10 . A quality research takes place in including phase dynamics, amplitude and phase scintillation parameters in algorithm of KF, Extended Kalman Filter (EKF) and Adaptive Kalman Filter (AKF) using AR method 5,8 . Harsha 11 ,et al also notifies the AEKF technique with ARx modelling that holds variable orders for amplitude and phase scintillations.…”

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confidence: 99%

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Fuzzy Logic-based Adaptive Extended Kalman Filter Algorithm for GNSS Receivers

Sreeharsha

Dabbakuti

2018

Def. Sc. Jl.

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Designing robust carrier tracking algorithms that are operable in strident environmental conditions for global navigation satellite systems (GNSS) receivers is the discern task. Major contribution in weakening the GNSS signals are ionospheric scintillations. The effect of scintillation can be known by amplitude scintillation index S 4 and phase scintillation index s f parameters. The proposed fuzzy logic based adaptive extended Kalman filter (AEKF) method helps in modelling the signal amplitude and phase dynamically by Auto-Regressive Exogenous (ARX) analysis using Sugeno fuzzy logic inference system. The algorithm gave good performance evaluation for synthetic Cornell scintillation monitor (CSM) data and real-time strong scintillated PRN 12 L1 C/A data on October 24 th , 2012 around 21:30 h, Brazil local time collected by GNSS software navigation receiver (GSNR'x). Fuzzy logic algorithm is implemented for selecting the ARX orders based on estimated amplitude and phase ionospheric scintillation observations. Fuzzy based AEKF algorithm has the capability to mitigate ionospheric scintillations under both geomagnetic quiet and disturbed conditions.

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“…An heuristic approach to adjust the Kalman gain has been proposed in [34] and a measurement noise adaptation using a C/N 0 estimator was analyzed in [35], a method which was further improved to sequentially adjust both noise statistics [36]. Recently, a ionospheric scintillation monitoring procedure was used to sequentially adjust the process noise covariance together with the C/N 0 estimation to adjust the measurement noise [37], and further improved to heuristically weight the resulting Kalman gain in [38]. Notice that all these techniques claim robustness against scintillation but none of them provides an effective scintillation mitigation procedure.…”

Section: Ionospheric Scintillation Mitigation and Gnss Carrier Tracmentioning

confidence: 99%

On the Mitigation of Ionospheric Scintillation in Advanced GNSS Receivers

Vilà‐Valls

Closas

Fernández‐Prades

et al. 2018

IEEE Trans. Aerosp. Electron. Syst.

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Ionospheric scintillation is one of the major threats and most challenging propagation scenarios affecting Global Navigation Satellite Systems (GNSS) and related applications. The fact that this phenomenon causes severe degradations only in equatorial and high latitude regions has led to very few contributions dealing with the fundamental scintillation mitigation problem, being of paramount importance in safety critical applications and advanced integrity receivers. The goal of this paper is twofold, first to bring together the most relevant contributions on GNSS receiver design under scintillation conditions, and then, to propose a new GNSS carrier tracking framework and scintillation mitigation methodology. Scintillation complex gain components are modeled as AR processes and embedded into the state-space formulation, providing the filter the capability to distinguish between dynamics and phase scintillation contributions. In addition, the actual need of robust solutions is solved by using an adaptive filtering approach and directly operating with the baseband received signal. Simulation results, using both synthetic and real scintillation data, are provided to support the theoretical discussion and to show the performance improvements of such new approach.

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Kalman Filter Based PLL Robust Against Ionospheric Scintillation

Cited by 9 publications

References 9 publications

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

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

Fuzzy Logic-based Adaptive Extended Kalman Filter Algorithm for GNSS Receivers

On the Mitigation of Ionospheric Scintillation in Advanced GNSS Receivers

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