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GNSS/5G Hybridization for Urban Navigation / Anne-Marie Tobie (2021)  

Public Titre : GNSS/5G Hybridization for Urban Navigation Type de document : Thèse/HDR Auteurs : Anne-Marie Tobie, Auteur ; Axel Javier Garcia Pena, Directeur de thèse ; Paul Thevenon, Directeur de thèse Editeur : Toulouse : Université Fédérale Toulouse Midi-Pyrénées Année de publication : 2021 Importance : 287 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse pour obtenir le doctorat de l'Université de Toulouse, Spécialité Informatique et Télécommunications Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] 4G [Termes IGN] 5G [Termes IGN] bruit blanc [Termes IGN] GNSS assisté pour la navigation [Termes IGN] milieu urbain [Termes IGN] modèle mathématique [Termes IGN] positionnement en intérieur [Termes IGN] positionnement par GNSS [Termes IGN] signal Galileo [Termes IGN] signal GPS [Termes IGN] simulation de signal [Termes IGN] temps de propagation [Termes IGN] trajet multiple Index. décimale : THESE Thèses et HDR Résumé : (Auteur) Over the past few years, the need for positioning, and thus the number of positioning services in general, has been in constant growth. This need for positioning has been increasingly focused on constrained environments, such as urban or indoor environments, where GNSS (Global Navigation Satellite System) is known to have significant limitations: multipath as well as the lack of Line-of-Sight (LOS) satellite visibility degrades the GNSS positioning solution and makes it unsuitable for some urban or indoor applications. In order to improve the GNSS positioning performance in constrained environments, many solutions are already available: hybridization with additional sensors, [1], [2] or the use of signals of opportunity (SoO) for example, [3], [4], [5], [6], [7], [8]. Concerning SoO, mobile communication signals, such as the 4G Long Term Evolution (LTE) or 5G, are naturally envisioned for positioning, [3], [9], [10]. Indeed, a significant number of users are expected to be “connected-users” and 5G systems offers promising opportunities. 5G technology is being standardized at 3GPP [11]; the first complete release of 5G specifications, Release-15, was provided to the community in March 2018. 5G is an emerging technology and its positioning performance, as well as a potential generic receiver scheme to conduct positioning operations, is still under analysis. In order to study the potential capabilities provided by 5G systems and to develop a 5G-based generic positioning module scheme, the first fundamental step is to develop mathematical models of the processed 5G signals at each stage of the receiver for realistic propagation channel models: the mathematical expression of the useful received 5G signal as well as the AWG (Additive White Gaussian) noise statistics. In the Ph.D., the focus is given to the correlation operation which is the basic function implemented by typical ranging modules for 4G LTE signals [12], DVB signals [7], [8], and GNSS [13]. In fact, the knowledge of the correlation output mathematical model could allow for the development of optimal 5G signal processing techniques for ranging positioning. Previous efforts were made to provide mathematical models of received signals at the different receiver signal processing stages for signals with similar structures to 5G signals – Orthogonal Frequency Division Multiplexing (OFDM) signals as defined in 3GPP standard, [14]. OFDM signal-type correlator output mathematical model and acquisition techniques were derived in [7], [15]. Moreover, in [8], [15], tracking techniques were proposed, analyzed and tested based on the correlator output mathematical model of [7]. However, these models were derived by assuming a constant propagation channel over the duration of the correlation. Unfortunately, when the Channel Impulse Response (CIR) provided by a realistic propagation channel is not considered to be constant over the duration of the correlation, the correlator output mathematical models are slightly different from the mathematical models proposed in [7], [8]. Therefore, the first main point considered in the Ph.D. consists in the development of mathematical models and statistics of processed 5G signals for positioning. In order to derive accurate mathematical models, the time evolution impact of the 5G standard compliant propagation channel is of the utmost importance. Note that, in the Ph.D., the continuous CIR will be approximated by a discretized CIR, and the continuous time-evolution will be replaced by the propagation channel generation sampling rate notion. This approximation makes sense since, in a real transmission/reception chain, the received time-continuous signal is, at the output of the Radio-Frequency (RF) front-end, sampled. Therefore, a preliminary step, prior to derive accurate mathematical models of processed 5G signals, consists in determining the most suitable CIR-generation sampling interval for a selected 5G standard compliant propagation channel, QuaDRiGa: trade-off between having a realistic characterization and its complexity. Complexity is especially important for 5G compliant channels with multiple emitter and receiver antennas, and high number of multipath. Then, the impact of a time-evolving propagation channel inside an OFDM symbol duration is studied. A method to select the most appropriate CIR sampling interval for accurate modelling of symbol demodulation, correlator outputs and delay tracking will also be proposed. Based on the correlator output mathematical models developed for realistic multipath environments for both GNSS and 5G systems, ranging modules are then developed. These ranging modules outputs the pseudo ranging measurements required to develop navigation solution. In order to improve the positioning availability and GNSS positioning performance in urban environment through the exploitation of 5G signals, both systems, GNSS and 5G communication systems, must be optimally combined. In fact, in order to achieve this optimal combination, both types of signals must be optimally processed, and the mathematical model of their generated pseudo range measurements must be accurately characterized. The second main objective of the Ph.D. aims thus at realistically characterizing GNSS and 5G pseudo range measurement mathematical models and at developing hybrid navigation modules exploiting/adapted to the derived pseudo range measurements mathematical models. In order to validate, the mathematical models developed in the Ph.D., a simulator is designed. The pseudo range measurements mathematical models are derived from a realistic simulator which integrates a typical GNSS receiver processing module and a typical 5G signal processing module proposition; moreover, in order to achieve a realistic characterization, the simulator implements highly realistic propagation channels for GNSS, SCHUN [16], and for 5G, QuaDRiGa [17] is developed. The hybrid navigation modules to be implemented and compared in this work are an Extended Kalman Filter (EKF) and an Unscented Kalman Filter (UKF). The performances of these hybrid navigation modules are then studied to quantify the improvements bringing by 5G TOA measurements. Note de contenu : 1- Introduction 2- GNSS signals, measurement model and positioning 3- 5G systems 4- Mathematical models and statistics of processed 5G signals for ranging based positioning for a realistic propagation channel 5- Synchronization module of a 5G signal 6- Characterization of pseudo range measurement errors due to propagation channels 7- Positioning in urban environment using 5G and GNSS measurements 8- Conclusion Numéro de notice : 26526 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT/URBANISME Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique et Télécommunications : Toulouse : 2021 Organisme de stage : Laboratoire de recherche ENAC nature-HAL : Thèse Date de publication en ligne : 09/04/2021 En ligne : https://hal.science/tel-03189527/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97534

Exploitation of hyperspectral data for assessing vegetation health under exposure to petroleum hydrocarbons / Guillaume Lassalle (2019)  

Public Titre : Exploitation of hyperspectral data for assessing vegetation health under exposure to petroleum hydrocarbons Type de document : Thèse/HDR Auteurs : Guillaume Lassalle, Auteur ; Arnaud Elger, Directeur de thèse ; Sophie Fabre, Directeur de thèse Editeur : Toulouse : Université Fédérale Toulouse Midi-Pyrénées Année de publication : 2019 Autre Editeur : Toulouse : Institut Supérieur de l’Aéronautique et de l’Espace Importance : 277 p. Format : 21 x 30 cm Note générale : bibliographie Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse délivré par l'Institut Supérieur de l’Aéronautique et de l’Espace, spécialité : Surfaces et interfaces continentales, Hydrologie Agrosystèmes, écosystèmes et environnement Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] canopée [Termes IGN] contamination [Termes IGN] feuille (végétation) [Termes IGN] hydrocarbure [Termes IGN] image aérienne [Termes IGN] image hyperspectrale [Termes IGN] indice de végétation [Termes IGN] modèle de transfert radiatif [Termes IGN] pollution des sols [Termes IGN] prospection pétrolière [Termes IGN] réflectance spectrale [Termes IGN] régression multiple [Termes IGN] signature spectrale [Termes IGN] surveillance de la végétation Index. décimale : THESE Thèses et HDR Résumé : (auteur) Oil exploration and contamination monitoring remain limited in regions covered by vegetation. Natural seepages and oil leakages due to facility failures are often masked by the foliage, making ineffective the current technologies used for detecting crude oil and petroleum products. However, the exposure of vegetation to oil affects its health and, consequently, its optical properties in the [400:2500] nm domain. This suggest being able to detect seepages and leakages indirectly, by analyzing vegetation health through its spectral reflectance. Based on this assumption, this thesis evaluates the potential of airborne hyperspectral imagery with high spatial resolution for detecting and quantifying oil contamination in vegetated regions. To achieve this, a three-step multiscale approach was adopted. The first step aimed at developing a method for detecting and characterizing the contamination under controlled conditions, by exploiting the optical properties of Rubus fruticosus L. The proposed method combines 14 vegetation indices in classification and allows detecting various oil contaminants accurately, from leaf to canopy scale. Its use under natural conditions was validated on a contaminated mud pit colonized by the same species. During the second step, a method for quantifying total petroleum hydrocarbons, based on inverting the PROSPECT model, was developed. The method exploits the pigment content of leaves, estimated from their spectral signature, for predicting the level of hydrocarbon contamination in soils accurately. The last step of the approach demonstrated the robustness of the two methods using airborne imagery. They proved performing for detecting and quantifying mud pit contamination. Another method of quantification, based on multiple regression, was proposed. At the end of this thesis, the three methods proposed were validated for use both on the field, at leaf and canopy scales, and on airborne hyperspectral images with high spatial resolution. Their performances depend however on the species, the season and the level of soil contamination. A similar approach was conducted under tropical conditions, allowing the development of a method for quantifying the contamination adapted to this context. In a perspective of operational use, an important effort is still required for extending the scope of the methods to other contexts and for anticipating their use on satellite- and drone-embedded hyperspectral sensors. Finally, the contribution of active remote sensing (radar and LiDAR) should be considered in further research, in order to overcome some of the limits specific to passive optical remote sensing. Note de contenu : General introduction 1- State-of-the-art of passive hyperspectral remote sensing for oil exploration and contamination monitoring in vegetated regions 2- Development of methods for detecting and quantifying oil contamination based on vegetation optical properties, under controlled conditions 3- Application and evaluation of the methods under natural conditions, from field scale to airborne hyperspectral imagery General conclusion Numéro de notice : 25946 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Spécialité : Surfaces et interfaces continentales, Hydrologie Agrosystèmes, écosystèmes et environnement : Toulouse : 2019 nature-HAL : Thèse DOI : sans En ligne : http://www.theses.fr/2019ESAE0030 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96343