On the identifiability of noise statistics and adaptive KF design for robust GNSS carrier tracking

Vilà‐Valls, Jordi; Closas, Pau; Fernández‐Prades, Carles

doi:10.1109/aero.2015.7118929

Cited by 24 publications

(30 citation statements)

References 40 publications

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“…The so-called Adaptive KFs (AKFs) try to sequentially adapt the filter parameters to the actual working conditions, providing an appealing solution to the previous suboptimal techniques. A global AKF design framework is given in [33]. 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].…”

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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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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“…Carrier Generator 1) Linear observation architectures: the inputs to the tracking block are directly phase observables, thus using the state-space model defined by (19) and (22). Note that this architecture is called standard linear KF throughout the paper, but in the literature it is also referred to as direct-state KF [16].…”

Section: Kf Discriminatormentioning

confidence: 99%

“…IfQ k > Q k 4 , the steady-state filter performance may be worse but the filter tends to be more reactive and robust to model changes, so more suitable to rapidly time-varying scenarios. Regarding the measurement noise,R k > R k implies that K k < K optimal , and thus the filter relies more on the process transition model [19].…”

Section: Kf Discriminatormentioning

confidence: 99%

“…The main drawback of KFs is the need of an exact knowledge of the system model noise statistics' for an optimal behavior, thus being constraint by the accuracy of the dynamic model and the a priori fixed system parameters. In practice, those quantities may need to be somehow adjusted to provide a robust solution [19], a problem which has been solved by the so-called adaptive KFs (AKFs) [19]- [24].…”

mentioning

confidence: 99%

“…Our previous experience on the use of KF synchronization schemes for both communications [25], [26] and positioning systems [19], [27]- [29], and the lack of a unified analysis in the literature and a clear answer to the PLL vs KF dilemma ignited this work. From different analysis carried out in the literature for specific scenarios, it seems clear that KF schemes should in general be preferred in front of PLL-based solutions, but the synchronization and aerospace communities seem still reluctant to go further on theoretical analysis of such techniques and to cross the gap between theory and implementation.…”

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confidence: 99%

See 2 more Smart Citations

Are PLLs dead? A tutorial on kalman filter-based techniques for digital carrier synchronization

Vilà‐Valls

Closas

Navarro

et al. 2017

IEEE Aerosp. Electron. Syst. Mag.

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Carrier synchronization is a fundamental stage in the receiver side of any communication or positioning system. Traditional carrier phase tracking techniques are based on well-known phase-locked loop (PLL) closed-loop architectures, which are still the methods of choice in modern receivers. Those techniques are well understood, easy to tune and perform well under benign propagation conditions, but their applicability is seriously compromised in harsh propagation environments where the signal may be affected by high dynamics, shadowing, strong fadings, multipath effects or ionospheric scintillation.From an optimal filtering standpoint, the Kalman filter (KF) is clearly a powerful alternative, but the synchronization community seems still reluctant to exploit all the potential it has to offer. The purpose of this paper is twofold: i) to review the basics and state-of-the-art on both PLL and KF-based tracking techniques, and ii) to present and justify the reasoning behind the systematic use of KF-based tracking approaches instead of the well-established PLL-based architectures from both theoretical and practical points of view. To support the discussion, two specific scenarios of interest to the aerospace community are numerically evaluated: robust carrier tracking of Global Navigation Satellite Systems' signals and synchronization in a deep space communications system.

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Noise covariance matrices in state‐space models: A survey and comparison of estimation methods—Part I

Duník

Straka

Kost

et al. 2017

Adaptive Control & Signal

118

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This paper deals with the estimation of the noise covariance matrices of systems described by state-space models. Stress is laid on the systematic survey and classification of both the recursive and batch processing methods proposed in the literature with a special focus on the correlation methods. Besides the correlation methods, representatives of other groups are introduced also with respect to their basic idea, estimate properties, assumptions and possible extensions, and user-defined parameters. Common and dual properties of the methods are highlighted, and a simulation comparison using exemplary MATLAB implementations of the methods is provided. KEYWORDSadaptive systems, noise covariance matrix, state estimation, state-space model, system identification INTRODUCTIONKnowledge of a system model is a key prerequisite for many state estimation, signal processing, fault detection, and optimal control problems. The model is often designed to be consistent with random behaviour of the system quantities and properties of the measurements. While the deterministic part of the model often arises from mathematical modelling on the basis of physical, chemical, or biological laws governing the behaviour of the system, the statistics of the stochastic part are often difficult to find by the modelling and have to be identified using the measured data. Incorrect description of the noise statistics may result in significant worsening of estimation, signal processing, detection, or control quality or even in a failure of the underlying algorithms.In the last 5 decades, therefore, a significant research interest has been focused on a design of the methods for the estimation of the properties of the stochastic part of the model. The attention has been devoted to both the input-output models 1-5 and the state-space (SS) models 6-14 and both recursive and batch processing methods. This paper focuses on the methods estimating the properties of the stochastic part of the system described by an SS model discrete in time. In particular, the methods estimating the covariance matrices (CMs) † of noises in the state and measurement equation from a sequence of measured data are of interest. The methods are further denoted as the noise CM estimation methods.

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On the identifiability of noise statistics and adaptive KF design for robust GNSS carrier tracking

Cited by 24 publications

References 40 publications

On the Mitigation of Ionospheric Scintillation in Advanced GNSS Receivers

On the Mitigation of Ionospheric Scintillation in Advanced GNSS Receivers

Are PLLs dead? A tutorial on kalman filter-based techniques for digital carrier synchronization

Noise covariance matrices in state‐space models: A survey and comparison of estimation methods—Part I

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