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Termes IGN > sciences naturelles > physique > traitement du signal > prétraitement du signal > convolution (signal)
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Titre : Signal Processing for GNSS Reflectometry Type de document : Thèse/HDR Auteurs : Corentin Lubeigt, Auteur ; Eric Chaumette, Directeur de thèse ; Jordi Vilà-Valls, Directeur de thèse Editeur : Toulouse : Institut Supérieur de l’Aéronautique et de l’Espace Année de publication : 2023 Importance : 217 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse pour obtenir le grade de Docteur de l'Université de Toulouse, Spécialité Informatique et TélécommunicationsLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] convolution (signal)
[Termes IGN] distorsion du signal
[Termes IGN] réflectométrie par GNSS
[Termes IGN] réflexion (rayonnement)
[Termes IGN] théorie de l'estimationIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Global Navigation Satellite Systems (GNSS) Reflectometry, or GNSS-R, is the study of GNSS signals reflected from the Earth’s surface. These so-called signals of opportunity, usually seen as a nuisance in standard navigation applications, contain meaningful information on the nature and relative position of the reflecting surface. Depending on the receiver platform (e.g., ground-based, airplane, satellite) and the reflecting surface itself (e.g., rough sea, lake), the reflected signal, more or less distorted, is difficult to model, and the corresponding methods to estimate the signal parameters of interest may vary. This thesis starts from the navigation multipath problem in harsh environments, which can be seen as a dual source estimation problem where the main source is the signal of interest, and the secondary one is a single reflection of the main source. Depending on the scenario and the resources at hand, it is possible i) to estimate the parameters of interest (i.e., time-delay, Doppler frequency, amplitude and phase) of both sources, or ii) to estimate only one source’s parameters, although these estimates may be biased because of the interfering source. Either way, it is necessary to know the achievable performance for these estimation problems. For this purpose, tools from the estimation theory, such as the Cramér-Rao bound (CRB), can be used. In this thesis a CRB expression was derived for the properly specified case (dual source), and the misspecified one (single source). These bounds were compared to the performance obtained with different estimators, in order to theoretically characterize the problem at hand. This study allowed to establish a clear mathematical framework that also fits the ground-based GNSS-R problem, for which the reflected signal is little distorted by the reflecting surface. In this case, the direct and reflected signals are close in time, which inevitably leads to interference, or crosstalk, and then to a clear performance degradation. Standard GNSS-R techniques, which do not perform well in this ground-based scenario, were compared to the CRB and two proposed approaches: i) a Taylor approximation of the dual source likelihood criterion when both sources are very close in time, and ii) a dual source estimation strategy to reduce or cancel the crosstalk. This part on ground-based GNSS-R was supported by a real data set, obtained from a data collection campaign organized by CNES (Toulouse, France). The problem changes slowly when the satellite elevation increases: the reflection, assumed coherent so far, turns non-coherent because of the reflecting surface roughness. The automatic detection of this transition (i.e., from coherent to non-coherent) is of great interest for future satellite missions. Reflection coherence is mainly observed by looking at the relative phase between the reflected and direct signals. Consequently, a statistical study of phase difference time series allowed to build tests that depend on the time series Gaussianity or regularity. The proposed tests were applied to a data set provided by the IEEC (Barcelona, Spain). Finally, for scenarios where the reflecting surface distorts the signal significantly, it is necessary to adapt the signal model. The approach proposed in this thesis is to consider the received signal as a convolution between the transmitted signal and the reflecting surface impulse response. This signal model goes with the derivation of the corresponding CRB and the implementation of the maximum likelihood estimator. The question of the impulse response size determination, that is, the determination of the number of pulses required to describe the impulse response, was also tackled based on hypothesis tests. Simulation results show the potential of this approach. Note de contenu :
Introduction
1. Concepts and Tools: From Estimation Theory to GNSS-R
1.1 Introduction
1.2 Background on Deterministic Estimation Theory
1.3 Global Navigation Satellite Systems
1.4 The Multipath Problem
1.5 GNSS Reflectometry
1.6 Conclusion
2. Multipath Effect and Its Impact on Positioning Performance
2.1 Introduction
2.2 MPEE for Different Multipath Mitigation Techniques
2.3 Joint Delay-Doppler Estimation Performance in a Dual Source Context
2.4 A Metric for Multipath-Robust Signal Design and Analysis
2.5 Misspecified Cramér-Rao Bounds in Multipath Scenarios
2.6 Conclusion
3. Ground-Based GNSS-R
3.1 Introduction
3.2 Gruissan Data Campaign
3.3 Crosstalk Characterization
3.4 Approximate Maximum Likelihood for Narrowband GNSS Signals
3.5 Performance on Simulated Data
3.6 Altimetry Using Wideband GNSS Signals
3.7 Conclusion
4. Towards Diffuse Scattering
4.1 Introduction
4.2 Coherence Analysis
4.3 Impulse Response Estimation
4.4 Impulse Response Size Determination: A Detection Problem
4.5 Conclusion
Conclusion and PerspectivesNuméro de notice : 26963 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique et Télécommunications : Toulouse : 2023 nature-HAL : Thèse DOI : sans Date de publication en ligne : 27/02/2023 En ligne : https://hal.science/tel-04006612v1/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102915 Unsupervised multi-level feature extraction for improvement of hyperspectral classification / Qiaoqiao Sun in Remote sensing, vol 13 n° 8 (April-2 2021)
Titre : Unsupervised multi-level feature extraction for improvement of hyperspectral classification Type de document : Article/Communication Auteurs : Qiaoqiao Sun, Auteur ; Xuefeng Liu, Auteur ; Salah Bourennane, Auteur Année de publication : 2021 Article en page(s) : n° 1602 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification non dirigée
[Termes IGN] codage
[Termes IGN] convolution (signal)
[Termes IGN] déconvolution
[Termes IGN] échantillonnage d'image
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] observation multiniveauxRésumé : (auteur) Deep learning models have strong abilities in learning features and they have been successfully applied in hyperspectral images (HSIs). However, the training of most deep learning models requires labeled samples and the collection of labeled samples are labor-consuming in HSI. In addition, single-level features from a single layer are usually considered, which may result in the loss of some important information. Using multiple networks to obtain multi-level features is a solution, but at the cost of longer training time and computational complexity. To solve these problems, a novel unsupervised multi-level feature extraction framework that is based on a three dimensional convolutional autoencoder (3D-CAE) is proposed in this paper. The designed 3D-CAE is stacked by fully 3D convolutional layers and 3D deconvolutional layers, which allows for the spectral-spatial information of targets to be mined simultaneously. Besides, the 3D-CAE can be trained in an unsupervised way without involving labeled samples. Moreover, the multi-level features are directly obtained from the encoded layers with different scales and resolutions, which is more efficient than using multiple networks to get them. The effectiveness of the proposed multi-level features is verified on two hyperspectral data sets. The results demonstrate that the proposed method has great promise in unsupervised feature learning and can help us to further improve the hyperspectral classification when compared with single-level features. Numéro de notice : A2021-380 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13081602 Date de publication en ligne : 20/04/2021 En ligne : https://doi.org/10.3390/rs13081602 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97628
in Remote sensing > vol 13 n° 8 (April-2 2021) . - n° 1602[article]
Titre : Applied signal processing Type de document : Guide/Manuel Auteurs : Sadasivan Puthusserypady, Auteur Editeur : Boston, Delft : Now publishers Année de publication : 2021 Collection : *NowOpen* Importance : 550 p. ISBN/ISSN/EAN : 978-1-68083-979-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] convolution (signal)
[Termes IGN] filtrage du signal
[Termes IGN] modulation de fréquence
[Termes IGN] série de Fourier
[Termes IGN] signal aléatoire
[Termes IGN] transformation de Fourier
[Termes IGN] transformation de HilbertRésumé : (éditeur) Being an inter-disciplinary subject, Signal Processing has application in almost all scientific fields. Applied Signal Processing tries to link between the analog and digital signal processing domains. Since the digital signal processing techniques have evolved from its analog counterpart, this book begins by explaining the fundamental concepts in analog signal processing and then progresses towards the digital signal processing. This will help the reader to gain a general overview of the whole subject and establish links between the various fundamental concepts. While the focus of this book is on the fundamentals of signal processing, the understanding of these topics greatly enhances the confident use as well as further development of the design and analysis of digital systems for various engineering and medical applications. Applied Signal Processing also prepares readers to further their knowledge in advanced topics within the field of signal processing. Note de contenu : 1- Introduction
2- Power and Energy
3- Fourier series
4- Fourier transform
5- Complex signals
6- Analog systems
7- Sampling and digital signals
8- Transform of discrete time signals
9- Fourier spectra of discrete-time signals
10- Digital systems
11- Implementation of digital systems
12- Discrete Fourier transform
13- Fast Fourier transform
14- Design of digital filters
15- Random signals
16- Modulation
17- Power Spectrum EstimationNuméro de notice : 28562 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Manuel de cours DOI : 10.1561/9781680839791 En ligne : http://dx.doi.org/10.1561/9781680839791 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97593 Kalman filtering with state constraints applied to multi-sensor systems and georeferencing / Sören Vogel (2020)
Titre : Kalman filtering with state constraints applied to multi-sensor systems and georeferencing Type de document : Thèse/HDR Auteurs : Sören Vogel, Auteur Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2020 Collection : DGK - C, ISSN 0065-5325 num. 856 Importance : 144 p. ISBN/ISSN/EAN : 978-3-7696-5268-0 Note générale : bibliographie
Diese Arbeit ist gleichzeitig veröffentlicht in: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Universität Hannover ISSN 0174-1454, Nr. 364, 2020Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] contrainte d'intégrité
[Termes IGN] convolution (signal)
[Termes IGN] étalonnage de capteur (imagerie)
[Termes IGN] géoréférencement direct
[Termes IGN] positionnement cinématique
[Termes IGN] programmation par contraintes
[Termes IGN] semis de pointsRésumé : (auteur) Active research on the development of autonomous vehicles has been carried out for several years now. However, some significant challenges still need to be solved in this context. Particularly relevant is the constant guarantee and assurance of the integrity of such autonomous systems. In order to ensure safe manoeuvring in the direct environment of humans, an accurate, precise, reliable and continuous determination of the vehicle’s position and orientation is mandatory. In geodesy, this process is also referred to as georeferencing with respect to a superordinate earth-fixed coordinate system. Especially for complex inner-city areas, there are no fully reliable methods available so far. The otherwise suitable and therefore common Global Navigation Satellite System (GNSS) observations can fail in urban canyons. However, this fact does not only apply exclusively to autonomous vehicles but can generally also be transferred to any kinematic Multi-Sensor System (MSS) operating within challenging environments. Especially in geodesy, there are many MSSs, which require accurate and reliable georeferencing regardless of the environment. This is indispensable for derived subsequent products, such as highly accurate three-dimensional point clouds for 3D city models or Building Information Modelling (BIM) applications. The demand for new georeferencing methods under aspects of integrity also involves the applicability of big data. Modern sensors for capturing the environment, e.g. laser scanners or cameras, are becoming increasingly cheaper and also offer higher information density and accuracy. For many kinematic MSSs, this change leads to a steady increase in the amount of acquired observation data. Many of the currently methods used are not suitable for processing such amounts of data, and instead, they only use a random subset. Besides, big data also influences potential requirements with regard to possible real-time applications. If there is no excessive computing power available to take into account the vast amounts of observation data, recursive methods are usually recommended. In this case, an iterative estimation of the requested quantities is performed, whereby the comprehensive total data set is divided into several individual epochs. If the most recent observations are successively available for each epoch, a filtering algorithm can be applied. Thus, an efficient estimation is carried out and, with respect to a comprehensive overall adjustment, generally larger observation sets can be considered. However, such filtering algorithms exist so far almost exclusively for explicit relations between the available observations and the requested estimation quantities.
If this mathematical relationship is implicit, which is certainly the case for several practical issues, only a few methods exist or, in the case of recursive parameter estimation, none at all. This circumstance is accompanied by the fact that the combination of implicit relationships with constraints regarding the parameters to be estimated has not yet been investigated at all. In this thesis, a versatile filter algorithm is presented, which is valid for explicit and for implicit mathematical relations as well. For the first time, methods for the consideration of constraints are given, especially for implicit relations. The developed methodology will be comprehensively validated and evaluated by simulations and real-world application examples of practical relevance. The usage of real data is directly related to kinematic MSSs and the related tasks of calibration and georeferencing. The latter especially with regard to complex innercity environments. In such challenging environments, the requirements for georeferencing under integrity aspects are of special importance. Therefore, the simultaneous use of independent and complementary information sources is applied in this thesis. This enables a reliable georeferencing solution to be achieved and a prompt notification to be issued in case of integrity violations.Note de contenu : 1- Introduction
2- Fundamentals of Recursive State-space Filtering
3- Methodological contributions
4- Kinematic Multi-sensor Systems and Their Efficient Calibration
5- Information-based Georeferencing
6- ConclusionsNuméro de notice : 17686 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD thesis : Geodäsie und Geoinformatik : Hanovre : 2020 DOI : sans En ligne : https://dgk.badw.de/fileadmin/user_upload/Files/DGK/docs/c-856.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98164
Titre : Comparison of filtering algorithms used for DTM production from airborne lidar data: a case study in Bergama, Turkey Type de document : Article/Communication Auteurs : Baris Suleymanoglu, Auteur ; Metin Soycan, Auteur Année de publication : 2019 Article en page(s) : pp 395 - 414 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme de filtrage
[Termes IGN] analyse comparative
[Termes IGN] convolution (signal)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] semis de points
[Termes IGN] test de performance
[Termes IGN] TurquieRésumé : (Auteur) A light detection and ranging (lidar) system is one of the most important technologies used for generating digital terrain models (DTMs). The point cloud data obtained by these systems consist of data gathered from ground and nonground features. To create a DTM with high resolution and accuracy, ground and nonground data must be separated. Numerous filtering algorithms have been developed for this purpose. The aim of this study was testing the filtering performance of six different filtering algorithms in four different test areas with different land cover were selected that had topographical features and characteristics. The algorithms were adaptive triangulated irregular network (ATIN), elevation threshold with an expand window (ETEW), maximum local slope (MLS), progressive morphology (PM), iterative polynomial fitting (IPF), and multiscale curvature classification (MCC) algorithms. In the results, all the filters performed well on a smooth surface and produced more errors in complex urban areas and rough terrain with dense vegetation. The IPF filtering algorithm generated the best results for the first three test areas (smooth landscape, urban areas and agricultural areas), while ETEW performed best in the fourth test area (steep areas with dense vegetation and infrastructure). Numéro de notice : A2019-502 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.15292/geodetski-vestnik.2019.03.395-414 En ligne : http://dx.doi.org/10.15292/geodetski-vestnik.2019.03.395-414 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93784
in Geodetski vestnik > vol 63 n° 3 (September - November 2019) . - pp 395 - 414[article]Réservation
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