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Conference Paper

Theoretical Evaluation of the GNSS Synchronization Performance Degradation under Interferences

Authors: Ortega Espluga Lorenzo, Vilà-Valls Jordi and Chaumette Eric

In Proc. 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, USA, September 19-23, 2022.

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Global Navigation Satellite Systems (GNSS) are a key player in a plethora of applications, ranging from navigation and timing, to Earth observation or space weather characterization. For navigation purposes, interference scenarios are among the most challenging operation conditions, which clearly impact the maximum likelihood estimates (MLE) of the signal synchronization parameters. While several interference mitigation techniques exist, a theoretical analysis on the GNSS MLE performance degradation under interference, being fundamental for system/receiver design, is a missing tool. The main goal of this contribution is to provide such analysis, by deriving closed-form expressions of the estimation bias, for a generic GNSS signal corrupted by an interference. The proposed bias are validated for a tone interference and a linear frequency modulation chirp interference.

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Signal and image processing / Localization and navigation and Space communication systems

Non-coherent CPM Detection under Gaussian Channel affected with Doppler Shift

Authors: Jerbi Anouar, Guilloud Frédéric, Amis Karine and Benaddi Tarik

In Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Virtual, September 12-15, 2022.

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We consider the transmission of a continuous phase modulated (CPM) signal through a Gaussian channel affected by Doppler shifts. We propose a receiver robust to the Doppler shifts derived from a non-coherent detection criterion. We compare its performance to another non-coherent receiver based on a linear approximation of the CPM signal (Laurent decomposition) to which we add a Doppler compensation. Simulation results show that the first algorithm is robust to low-moderate Doppler shifts, while the second is robust to any one. We finally compare these two algorithms to delay-optimized differential detectors which do not require any Doppler shift estimation. We also provide complexity estimations to guide the possible complexity-performance trade-offs.

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Digital communications / Space communication systems

Technical Note

Technical Note - Developments for MCRB Computation in Multipath Scenarios

Authors: Lubeigt Corentin, Ortega Espluga Lorenzo, Vilà-Valls Jordi and Chaumette Eric

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This is a supplementary material associated with the article "Untangling first and second order statistics contributions in multipath scenarios" that can be found, in the online version, at doi: https://doi.org/10.1016/j.sigpro.2022.108868.

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Signal and image processing / Localization and navigation

Conference Paper

Détection Non-cohérente des Modulations CPM en Présence d’un Décalage Doppler.

Authors: Jerbi Anouar, Amis Karine, Guilloud Frédéric and Benaddi Tarik

In Proc. Groupement de Recherche en Traitement du Signal et des Images (GRETSI), Nancy, France, September 6-9, 2022.

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We consider the transmission of a continuous phase modulated (CPM) signal through a Gaussian channel affected by Doppler shifts. We focus on a receiver robust to the Doppler shift by proposing two different types of receiver derived from a non-coherent detection criterion : one based on a linear approximation of the CPM signal (Laurent decomposition) and the other based on its exact expression. Simulation results show that the first algorithm is robust to low-moderate Doppler shifts, while the second is robust to any one.

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Digital communications / Space communication systems

Les Signaux à Bande Large au Service de la Réflectométrie par GNSS à Site Bas

Authors: Lubeigt Corentin, Vilà-Valls Jordi, Lestarquit Laurent and Chaumette Eric

In Proc. Groupe de Recherche et d'Etudes de Traitement du Signal et des Images (GRETSI), Nancy, France, September 5-9, 2022.

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Pendant plus de trente ans, les signaux Global Navigation Satellite System (GNSS) ont été utilisés comme signaux d’opportunité comme en GNSS Reflectometry (GNSS-R). L’étude de la réflexion de ces signaux sur le sol peut en effet conduire à l’estimation de paramètres sur la surface de réflexion ou sur la hauteur du récepteur. Lorsque cette hauteur est faible, le récepteur est à site bas et la proximité du sol entraîne de fortes interférences entre les signaux direct et réfléchi ce qui rend difficile une estimation non biaisée des différentes observables. Cette difficulté peut néanmoins être levée grâce à des signaux GNSS occupant des bandes de plus en plus larges. For more than three decades, Global Navigation Satellite System (GNSS) signals have been seen as signals of opportunity as in GNSS Reflectometry (GNSS-R). The study of the reflections from the ground of such signals can indeed lead to many features regarding the reflecting surface and the receiver’s height. When this height is small, the receiver is said ground-based and the vicinity to the ground induces important interferences between the direct and the reflected path which make it difficult to process to obtain an unbiased altimetry product. However, this difficulty can be leveraged thanks to recent wideband GNSS signals.

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Signal and image processing and Networking / Localization and navigation

Réseaux récurrents d’attention pour la régression de séries temporelles

Authors: Perrier Victor, Lochin Emmanuel, Tourneret Jean-Yves and Gélard Patrick

In Proc. Groupe de Recherche et d'Etudes de Traitement du Signal et des Images (GRETSI), Nancy, France, September 5-9, 2022.

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Cet article étudie une nouvelle architecture récurrente basée sur l’attention, plus légère et moins coûteuse en temps de calcul qu’un réseau d’attention global. Nous détaillons en quoi ce type d’architecture permet d’atteindre de meilleures performances que des réseaux récurrents plus classiques, dans le cas de la régression de séries temporelles. Nous montrons son intérêt pour la prédiction de l’état d’un réseau de communication, et plus particulièrement pour la détection de la congestion.

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Networking / Space communication systems

Estimation du paramètre de multifractalité : régression linéaire, maximum de vraisemblance ou inférence Bayésienne ?

Authors: Leon Arencibia Lorena, Wendt Herwig, Tourneret Jean-Yves and Abry Patrice

In Proc. XXVIIIème Groupe de Recherche et d'Etudes de Traitement du Signal et des Images (GRETSI), Nancy, France, September 6-9, 2022.

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L’analyse multifractale est aujourd’hui un outil important de la caractérisation des dynamiques temporelles ou spatiales. Si l’estimation du paramètre de multifractalité peut se faire efficacement par des outils devenus standards, elle reste délicate pour des signaux et images de petites tailles. Le présent travail propose différents estimateurs construits sur des algorithmes Expectation-Maximization et compare leurs performances à l’aide de simulations de Monte Carlo contre les outils de l’état de l’art dans un contexte de signaux univariés de petites tailles.

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Signal and image processing / Other

Estimation du centre et du rayon d'une hypersphère à l'aide d'une loi a priori de Von Mises-Fisher et d'un algorithme EM

Authors: Lesouple Julien, Pilastre Barbara, Altmann Yoann and Tourneret Jean-Yves

In Proc. XXVIII ème Colloque Francophone de Traitement du Signal et des Images (GRETSI), Nancy, France, September, 2022.

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Cet article présente une extension d'un algorithme EM (expectation maximization) publié récemment par les auteurs permettant d'estimer conjointement le centre et le rayon d'une hypersphère avec les hyperparamètres d'un modèle statistique prenant en compte le fait que les observations sont localisées sur une partie de l'hypersphère. La méthode proposée repose sur l'ajout de variables latentes ayant une loi a priori de von Mises-Fisher. Ce modèle statistique permet d'exprimer la vraisemblance complète des données, dont l'espérance conditionnée aux données observées possède une distribution connue conduisant à un algorithme EM simple et efficace. Les performances de cet algorithme d'estimation sont évaluées à l'aide de de simulations effectuées dans un cas bi-dimensionnel avec des résultats prometteurs.

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Signal and image processing / Earth observation and Other

Journal Paper

Non-Binary PRN-Chirp Modulation: A GNSS Fast Acquisition Signal Waveform

Authors: Ortega Espluga Lorenzo, Vilà-Valls Jordi and Chaumette Eric

IEEE Communications Letters, vol. 26, Issue 9, pp. 2151-2155, September, 2022.

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In this article, we propose a new non-binary modulation which allows both Global Navigation Satellite Systems (GNSS) synchronization and the demodulation of non-binary symbols, without the need of a pilot signal, with the aim to provide a fast first position, velocity and time fix. The waveform is constructed as the product of i) a pseudo-random noise sequence with good auto-correlation and cross-correlation properties, and ii) a chirp spread spectrum family, which allows to demodulate non-binary symbols even if the signal phase is unknown. In order to demodulate the data, a bank of non-coherent matched filters is proposed. Because of the particular modulation structure, the receiver is capable to demodulate the navigation message faster while allowing the basic GNSS signal processing functionalities. Illustrative results are provided to support the discussion.

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Signal and image processing / Localization and navigation and Space communication systems

Conference Paper

Robust Estimation of Gaussian Mixture Models Using Anomaly Scores and Bayesian Information Criterion for Missing Value Imputation

Authors: Mouret Florian, Albughdadi Mohanad Y.S., Duthoit Sylvie, Kouamé Denis and Tourneret Jean-Yves

In Proc. 30th European Signal Processing Conference (EUSIPCO), Belgrade, Serbia, August 29-September 2, 2022.

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The Expectation-Maximization algorithm is a very popular approach for estimating the parameters of Gaussian mixture models (GMMs). A known issue with GMM estimation is its sensitivity to outliers, which can lead to poor estimation performance depending on the dataset under consideration. A common approach to deal with this issue is robust estimation, which typically consists of reducing the influence of the outliers on the estimators by weighting the impact of some samples of the dataset considered as outliers. In an unsupervised context, it is difficult to know which sample from the database corresponds to a normal observation. To that extent, we propose to use within the EM algorithm an outlier detection step that attributes an anomaly score to each sample of the database in an unsupervised way. A modified Bayesian Information Criterion is also introduced to efficiently select the appropriate amount of outliers contained in a dataset. The proposed method is tested on a benchmark remote sensing dataset coming from the UCI Machine Learning Repository. The experimental results show the interest of the proposed robustification when compared to other benchmark imputation procedures.

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Signal and image processing / Other

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