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41	Communications coopératives dans les réseaux autour du corps humain pour la capture du mouvement / Cooperatif communications with wireless body area networks for motion capture Jimenez Guizar, Arturo Mauricio 27 September 2016 (has links) Les réseaux corporels (WBAN) se réfère aux réseaux de capteurs (WSN) "portables" utilisés pour collecter des données personnelles, telles que la fréquence cardiaque ou l'activité humaine. Cette thèse a pour objectif de proposer des algorithmes coopératifs (PHY/MAC) pour effectuer des applications de localisation, tels que la capture de mouvement et la navigation de groupe. Pour cela, nous exploitons les avantages du WBAN avec différentes topologies et différents types de liens: on-body à l'échelle du corps, body-to-body entre les utilisateurs et off-body par rapport à l'infrastructure. La transmission repose sur une radio impulsionnelle (IR-UWB), afin d'obtenir des mesures de distance précises, basées sur l’estimation du temps d'arrivée (TOA). Ainsi, on s’intéresse au problème du positionnement à travers de la conception de stratégies coopératives et en considérant la mobilité du corps et les variations canal. Notre première contribution consiste en la création d'une base de données obtenue avec de scénarios réalistes pour la modélisation de la mobilité et du canal. Ensuite, nous introduisons un simulateur capable d'exploiter nos mesures pour la conception de protocoles. Grâce à ces outils, nous étudions d’abord l'impact de la mobilité et des variations de canal sur l'estimation de la distance avec le protocole "three way-ranging" (3-WR). Ainsi, nous quantifions et comparons l'erreur avec des modèles statistiques. Dans un second temps, nous analysons différentes algorithmes de gestion de ressources pour réduire l'impact de la mobilité sur l'estimation de position. Ensuite, nous proposons une optimisation avec un filtre de Kalman étendu (EKF) pour réduire l'erreur. Enfin, nous proposons un algorithme coopératif basé sur l'analyse d’estimateurs de qualité de lien (LQEs) pour améliorer la fiabilité. Pour cela, nous évaluons le taux de succès de positionnement en utilisant trois modèles de canaux (empirique, simulé et expérimental) avec un algorithme (basé sur la théorie des jeux) pour le choix des ancres virtuelles. / Wireless Body Area Networks (WBAN) refers to the family of “wearable” wireless sensor networks (WSN) used to collect personal data, such as human activity, heart rate, sleep sequences or geographical position. This thesis aims at proposing cooperative algorithms and cross-layer mechanisms with WBAN to perform large-scale individual motion capture and coordinated group navigation applications. For this purpose, we exploit the advantages of jointly cooperative and heterogeneous WBAN under full/half-mesh topologies for localization purposes, from on-body links at the body scale, body-to-body links between mobile users of a group and off-body links with respect to the environment and the infrastructure. The wireless transmission relies on an impulse radio Ultra-Wideband (IR-UWB) radio (based on the IEEE 802.15.6 standard), in order to obtain accurate peer-to-peer ranging measurements based on Time of Arrival (ToA) estimates. Thus, we address the problem of positioning and ranging estimation through the design of cross-layer strategies by considering realistic body mobility and channel variations. Our first contribution consists in the creation of an unprecedented WBAN measurement database obtained with real experimental scenarios for mobility and channel modelling. Then, we introduce a discrete-event (WSNet) and deterministic (PyLayers) co-simulator tool able to exploit our measurement database to help us on the design and validation of cooperative algorithms. Using these tools, we investigate the impact of nodes mobility and channel variations on the ranging estimation. In particular, we study the “three-way ranging” (3-WR) protocol and we observed that the delays of 3-WR packets have an impact on the distances estimated in function of the speed of nodes. Then, we quantify and compare the error with statistical models and we show that the error generated by the channel is bigger than the mobility error. In a second time, we extend our study for the position estimation. Thus, we analyze different strategies at MAC layer through scheduling and slot allocation algorithms to reduce the impact of mobility. Then, we propose to optimize our positioning algorithm with an extended Kalman filter (EKF), by using our scheduling strategies and the statistical models of mobility and channel errors. Finally, we propose a distributed-cooperative algorithm based on the analysis of long-term and short-term link quality estimators (LQEs) to improve the reliability of positioning. To do so, we evaluate the positioning success rate under three different channel models (empirical, simulated and experimental) along with a conditional algorithm (based on game theory) for virtual anchor choice. We show that our algorithm improve the number of positions estimated for the nodes with the worst localization performance. Télécommunications Réseaux corporels - WBAN Communications coopératives Capture du mouvement Standard IEEE 802.151.6 Mesure de distance point à point Telecommunications Wireless body area networks Cooperative communications Motion capture Impulse radio ultra-Wideband IR-UWB Standard IEEE 802.151.6 Time for arrival estimates - ToA Location-Based services - LBS 621.384 072
42	Channel Probing for an Indoor Wireless Communications Channel Hunter, Brandon 13 March 2003 (has links) (PDF) The statistics of the amplitude, time and angle of arrival of multipaths in an indoor environment are all necessary components of multipath models used to simulate the performance of spatial diversity in receive antenna configurations. The model presented by Saleh and Valenzuela, was added to by Spencer et. al., and included all three of these parameters for a 7 GHz channel. A system was built to measure these multipath parameters at 2.4 GHz for multiple locations in an indoor environment. Another system was built to measure the angle of transmission for a 6 GHz channel. The addition of this parameter allows spatial diversity at the transmitter along with the receiver to be simulated. The process of going from raw measurement data to discrete arrivals and then to clustered arrivals is analyzed. Many possible errors associated with discrete arrival processing are discussed along with possible solutions. Four clustering methods are compared and their relative strengths and weaknesses are pointed out. The effects that errors in the clustering process have on parameter estimation and model performance are also simulated. multipath multipath propagation propagation environment indoor propagation indoor multipath indoor multipath propagation channel probing channel probing indoor wireless communications channel indoor wireless communications indoor wireless communications wireless communications wireless communications channel MIMO SISO multiple input multiple output single input single output antenna diversity antenna diversity antenna array channel modeling modeling model wireless propagation clustering methods clustering algorithms clustering parameter estimation parameter channel matrix h matrix ray tracing point spread function psf deconvolution clean algorithm clean algorithm antenna pattern network analyzer temporal pattern 2.4 ghz system 6 ghz system channel capacity capacity estimate channel capacity estimate propagation environment scattering transfer matrix matrix water filling solution water filling method water filling Saleh Valenzuela Saleh and Valenzuela Model arrival time of arrival angle of arrival direction of arrival arrival amplitude arrivals antenna positioner data simulations measurements angle of departure direction of departure correlation convolution errors error CFA CFAD constant false alarm constant false alarm detection signal signal dependent clutter clutter returns transmitter transmit receive receiver transmit antenna receive antenna spatial correlation system PSF system point spread function mapping fuzzy c-means clustering fuzzy clustering c-means clustering c-means fuzzy k-means euclidean distance time amplitude delay performance prediction performance prediction errors fuzzy weighted distance method rectangular method weighted rectangular method statistics statistical statistical model Electrical and Computer Engineering

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Communications coopératives dans les réseaux autour du corps humain pour la capture du mouvement / Cooperatif communications with wireless body area networks for motion capture

Channel Probing for an Indoor Wireless Communications Channel