This thesis investigates the possibilities of implementing a location awareness mechanism for the so-called lesswire localNavigator. The author claims that it is possible to implement such a mechanism within the given prerequisites and constraints, even though with today’s technology it may not be economically feasible. Due to the lesswire’s constraints: high accuracy (67%), high-resolution (12 m2) and no hardware modification allowed to the mobile device, the suggested scheme uses Time Difference Of Arrival technology (TDOA). The main advantage of TDOA, as stated in this thesis, is the fact that it is almost totally independent of the preferred wireless technology of the mobile device. TDOA technology therefore, can be applied to a wide range of wireless networks (primarily TDMA, CDMA, FDMA – based). A disadvantage of this scheme is the fact that the network infrastructure needs to be extremely well synchronized - which in turn implies higher costs. Depending on how the synchronization problem is solved, the proposed system may well be economically feasible in the near future.

Under det senaste decenniet har inomhuspositionering fått en ökad popularitet och stått i fokus för forskning och utveckling, eftersom det ger praktiska möjligheter till att spåra och navigera objekt och människor i inomhusmiljöer. Det finns ingen global lösning för inomhuspositionering baserat på en enstaka teknologi såsom det gör för utomhuspositionering med sin satellitbaserade globala positioneringssystem. Många inomhusteknologier står inför många utmaningar såsom låg positioneringsnoggrannhet samt dyr och stor hårdvara. Den här uppsatsen beskriver hur en simpel och kostnadseffektiv lösning, som addresserar problemen med noggrannheten och hårdvarukostnaden, genom en iterativ forskningsmetod, utvecklades. Vår lösning är ett ultraljudsbaserat passivt sändare-mottagare system som kombinerar multilateration som positioneringsteknik och tidsskillnad av ankomst (TDOA) som mätprincip för att beräkna en 3D-position inuti en 4x2x2 m testyta med en övergripande noggrannhet på 16 cm inom ett 95% konfidensintervall. Vi registrerade noggranna TDOA-värden med en komparatorkrets som fungerade som en amplitud-trigger. Det här tillvägagångssättet var mycket enklare än vad andra relaterade arbeten använde sig av, vilket var sampling för att bearbeta inkommande signaler från sändarna. / During the past decade, indoor positioning has gained more popularity and has become a focus of research and development as it provides practical possibilities to track and navigate objects and people in indoor environments. There is no overall solution for indoor positioning based on a single technology like the solution for outdoor positioning with its satellite-based global positioning system. Many indoor positioning technologies today face many challenges such as low positioning accuracy, expensive and large hardware. This thesis describes how a simple and cost-effective solution, that addresses the problem of accuracy and space cost with regards to hardware being used, was developed through an iterative research methodology. Our solution is an ultrasound-based passive receiver-transmitter system that combines multilateration as a positioning technique and time difference of arrival (TDOA) as a measuring principle. This combination is used to calculate a 3D position within a 4x2x2 m test area with an overall accuracy of 16 cm within a 95% confidence interval. We registered accurate TDOA values with a comparator circuit that acts as an amplitude trigger. This approach was much more simple than that of other related works which used sampling to process incoming signals from the transmitters.

A partial discharge (PD) is the dissipation of energy caused by the buildup of localized electric field intensity. In high voltage devices such as transformers, this buildup of charge and its release can be symptomatic of problems associated with aging, such as floating components and insulation breakdown. This is why PD detection is used in power systems to monitor the state of health of high voltage transformers. If such problems are not detected and repaired, the strength and frequency of PDs increases and eventually leads to the catastrophic failure of the transformer, which can cause external equipment damage, fires and loss of revenue due to an unscheduled outage. Reliable online PD detection is a critical need for power companies to improve personnel safety and decrease the potential for loss of service. The PD phenomenon is manifested in a variety of physically observable signals including electric and acoustic pulses and is currently detected using a host of exterior measurement techniques. These techniques include electrical lead tapping and piezoelectric transducer (PZT) based acoustic detection. Many modern systems use a combination of these techniques because electrical detection is an older and proven technology and acoustic detection allows for the source to be located when several sensors are mounted to the exterior of the tank. However, if an acoustic sensor could be placed inside the tank, not only would acoustic detection be easier due to the increased signal amplitude and elimination of multipath interference, but positioning could also be performed with more accuracy in a shorter time. This thesis presents a fiber optic acoustic sensing system design that can be used to detect and locate PD sources within a high voltage transformer. The system is based on an optical acoustic (OA) sensor that is capable of surviving the harsh environment of the transformer interior while not compromising the transformer's functionality, which allows for online detection and positioning. This thesis presents the theoretical functionality and experimental validation of a band-limited OA sensor with a usable range of 100-300 kHz, which is consistent with the frequency content of an acoustic pulse caused by a PD event. It also presents a positioning system using the time difference of arrival (TDOA) of the acoustic pulse with respect to four sensors that is capable of reporting the three-dimensional position of a PD to within ±5cm on any axis. / Master of Science

Power transformers are one of the most important components in the electrical energy network. Extending transformer life is very economically valuable due to power outage. Therefore the development of instruments to monitor the transformer condition is of great interest. Detection of partial discharges (PDs) in power transformers is an effective diagnostic because it may reveal and quantify an important aging factor and provide information on the condition of the transformer. However, partial discharge diagnostics are still not effectively used for online monitoring of transformers because of the complexity of PD measurements and difficulties of discriminating of PDs and other noise sources. This thesis presents a further study of detection and location of partial discharges in power transformers based on previous work conducted at the Center for Photonics Technology (CPT) at Virginia Tech. The detection and positioning system consists of multiple extrinsic Fabry-Parot interferometric (EFPI) fiber acoustic sensors which can survive the harsh environment of oil-filled transformers. This thesis work is focused on optimal arrangement of multiple sensors to monitor and locate PD activities in a power transformer. This includes the following aspects. First, the sensor design requirements are discussed in order to successfully detect and accurately position the PD sources. In the following sections, Finite Element Method (FEM) is used to model the EFPI sensor fabricated at CPT. Experiments were conducted to measure the angular dependence of the frequency response of the sensor. It is shown that within the range of ±45º incident angles, the sensitivity varies by 3-5dB. Finally, the thesis demonstrates a PD positioning experiment in a 500 gallon water tank (R à H = 74" à 30" cylinder) using a hyperbolic positioning algorithm and time difference of arrival (TDOA). Finally we demonstrated that 100% of the positioning data is bounded by a 22.7à 4.1à 5.3 mm₃ cube, with a sensing range of 810 mm using the leading edge method with FIR filtering. / Master of Science

The angular location of an acoustic source can be estimated by measuring an acoustic direction of incidence based solely on the noise produced by the source. Methods for determining the direction of incidence based on sound intensity, the phase of cross-spectral functions, and cross-correlation functions are available. In this current work, we implement Dominant Frequency SElection (DFSE) algorithm. Direction of arrival (DOA) estimation usingmicrophone arrays is to use the phase information present in signals from microphones that are spatially separated. DFSE uses the phase difference between the Fourier transformedsignals to estimate the direction ofarrival (DOA)and is implemented using a three-element ’L’ shaped microphone array, linear microphone array, and planar 16-microphone array. This method is based on simply locating the maximum amplitude from each of the Fourier transformed signals and thereby deriving the source location by solving the set of non-linear least squares equations. For any pair of microphones, the surface on whichthe time difference ofarrival (TDOA) is constant is a hyperboloidoftwo sheets. Acoustic source localization algorithms typically exploit this fact by grouping all microphones into pairs, estimating the TDOA of each pair, then finding the point where all associated hyperboloids most nearly intersect. We make use of both closed-form solutions and iterative techniques to solve for the source location.Acoustic source positioned in 2-dimensional plane and 3-dimensional space have been successfully located.

Autant le GPS tend à s'imposer pour la localisation à l'extérieur des bâtiments, autant la situation est beaucoup plus ouverte pour la localisation à l'intérieur des bâtiments. De nombreux réseaux WiFi sont déployés dans les bâtiments. Ils diffusent des informations de puissance du signal permettant de remonter à la position d'un mobile. La technique du fingerprinting par puissance WiFi permet de localiser le mobile. Cependant, l'utilisation de cette technique de localisation requière une base de données correspondant à la couverture radio WiFi dans l'environnement.<br />L'utilisation d'une technique de localisation basée sur des mesures temporelles est moins contraignante que le fingerprinting. L'émission d'impulsions radio très brèves confère à la technologie 802.15.4a un fort pouvoir séparateur des multi-trajets. Le phénomène de multi-trajets est la principale contrainte au déploiement d'une technologie de localisation par mesures temporelles. La détection du premier trajet est très importante.<br />Des estimateurs comme le filtre de Kalman ou le filtre particulaire sont nécessaires pour limiter les effets des multi-trajets, des bruits de mesure, etc. Ces filtres peuvent aussi intégrer des informations de cartographie. Bien souvent, l'exploitation d'une seule technologie est insuffisante. La fusion d'informations de localisation est une étape supplémentaire pour améliorer la localisation. Des architectures de fusion robustes permettent de corriger les défauts de chacune des technologies pour conduire à un système plus robuste et plus précis en toutes circonstances.<br />Ce travail présente une approche innovante pour la localisation WiFi avec l'exploitation de cartographie dans l'estimateur tout en gardant une faible complexité suivant la plate-forme de déploiement visée. L'exploration des capacités de la localisation par ULB est proposée dans un second temps, avant d'aborder une réflexion sur les méthodes de fusion multi-capteurs.

Le but de cette thèse est l'étude et la mise en oeuvre d'un système de localisation sous-marine compact et simple à mettre en place pour une utilisation en zones portuaires, côtières et environnements peu profonds. Nous proposons un système SBL (Small Distance Baseline) avec un nombre réduit de transducteurs (une balise d'émission et deux hydrophones). La configuration géométrique du système étant contraignante (hydrophones proches) la précision du positionnement obtenue par les méthodes classiques est faible. Nous proposons une nouvelle méthode de localisation améliorant la précision. La balise à localiser émet un signal à étalement de spectre. La différence de distance entre les trajets des signaux des hydrophones est mesurée par corrélation. Nous proposons deux boucles de poursuites pour l'estimation conjointe de la fréquence Doppler et du délai entre les signaux reçus. Ces techniques de poursuite basées sur un filtre de Kalman sont implémentées en boucle fermée et ouverte. Les observations TDOA (Time Difference Of Arrival) conduisent à utiliser une technique de localisation hyperbolique. Nous proposons une représentation statistique qui exploite la géométrie de notre système de mesure pour déterminer une zone de localisation probable autour de chaque hyperbole. En utilisant des positions de bateau successives, on construit une densité de probabilité dont le maximum définit la position de la balise. On montre sur données synthétiquesque pour un bruit de mesure réaliste, il est possible de déterminer la position de la balise avec une précision submétrique. Les expérimentations réelles confirment la faisabilité du système et la précision obtenue est dans ce cas métrique. / The purpose of that thesis work is the research and implementation of a compact and simple underwater localization system that aim to be used in ports, coastal areas and other low-depth environments. Our system is SBL (Small Distance Baseline), with a small number of transceivers (only one emitter and two hydrophones). Due to the system's geometric configuration not being optimal (both hydrophones are close to one another), the precision obtained using classical approaches is poor. We therefore propose a new localization approach that will improve it. The emitter we wish to localize emit a spread spectrum signal. The time difference of arrival (TDOA) between the two hydrophones is then determined using correlations methods. We propose in our work two tracking loops that will estimate both the delay and the doppler frequency between the signals. Using a Kalman filter , those methods are implemented respectively in open and close loop. From each TDOA measurement, we can calculatea hyperbolic area of possible emitter location. We thus use a statistical model which takes into account the local geometry of our measurements system in order to create a probable localization area around each hyperbole. By using the measurements at several different boat positions, we create a probability density whose maximum will be centered around the emitter's position. We show that, on simulated data, it is possible to localize the beacon with a precision beneath a meter with a realistic noise level. Experimental work and real data collection confirm that the method can in that context achieve the same result with a precision of a few meters.

Indoor positioning is desired in many areas for various reasons, such as positioning products in industrial environments, hospital equipment or firefighters inside a building on fire. One even tougher situation where indoor positioning can be useful is locating a specific object on a shelf in a commercial setting. This thesis aims to investigate and design different network deployment strategies in an indoor environment in order to achieve both high position estimation accuracy and availability. The investigation considers the two positioning techniques downlink time difference of arrival, DL-TDOA, and round trip time, RTT. Simulations of several deployments are performed in two standard scenarios which mimic an indoor open office and an indoor factory, respectively. Factors having an impact on the positioning accuracy and availability are found to be deployment geometry, number of base stations, line-of-sight conditions and interference, with the most important being deployment geometry. Two deployment strategies are designed with the goal of optimising the deployment geometry. In order to achieve both high positioning accuracy and availability in a simple, sparsely cluttered environment, the strategy is to deploy the base stations evenly around the edges of the deployment area. In a more problematic, densely cluttered environment the approach somewhat differs. The proposed strategy is now to identify and strategically place some base stations in the most cluttered areas but still place a majority of the base stations around the edges of the deployment area. A robust positioning algorithm is able to handle interference well and to decrease its impact on the positioning accuracy. The cost, in terms of frequency resources, of using more orthogonal signals may not be worth the small improvement in accuracy and availability.

Spatial hearing is crucial in real life but deteriorates in participants with severe sensorineural hearing loss or single-sided deafness. This ability can potentially be improved with a unilateral cochlear implant (CI). The present study investigated measures of sound localization in participants with single-sided deafness provided with a CI. Sound localization was measured separately at eight loudspeaker positions (4°, 30°, 60°, and 90°) on the CI side and on the normal-hearing side. Low- and high-frequency noise bursts were used in the tests to investigate possible differences in the processing of interaural time and level differences. Data were compared to normal-hearing adults aged between 20 and 83. In addition, the benefit of the CI in speech understanding in noise was compared to the localization ability. Fifteen out of 18 participants were able to localize signals on the CI side and on the normal-hearing side, although performance was highly variable across participants. Three participants always pointed to the normal-hearing side, irrespective of the location of the signal. The comparison with control data showed that participants had particular difficulties localizing sounds at frontal locations and on the CI side. In contrast to most previous results, participants were able to localize low-frequency signals, although they localized high-frequency signals more accurately. Speech understanding in noise was better with the CI compared to testing without CI, but only at a position where the CI also improved sound localization. Our data suggest that a CI can, to a large extent, restore localization in participants with single-sided deafness. Difficulties may remain at frontal locations and on the CI side. However, speech understanding in noise improves when wearing the CI. The treatment with a CI in these participants might provide real-world benefits, such as improved orientation in traffic and speech understanding in difficult listening situations.

Dans cette thèse, nous proposons des modèles probabilistes et statistiques d’analyse de données multidimensionnelles pour la prévision de l’incertitude sur les trajectoires d’aéronefs. En supposant que pendant le vol, chaque aéronef suit sa trajectoire 3D contenue dans son plan de vol déposé, nous avons utilisé l’ensemble des caractéristiques de l’environnement des vols comme variables indépendantes pour expliquer l’heure de passage des aéronefs sur les points de leur trajectoire de vol prévue. Ces caractéristiques sont : les conditions météorologiques et atmosphériques, les paramètres courants des vols, les informations contenues dans les plans de vol déposés et la complexité de trafic. Typiquement, la variable dépendante dans cette étude est la différence entre les instants observés pendant le vol et les instants prévus dans les plans de vol pour le passage des aéronefs sur les points de leur trajectoire prévue : c’est la variable écart temporel. En utilisant une technique basée sur le partitionnement récursif d’un échantillon des données, nous avons construit quatre modèles. Le premier modèle que nous avons appelé CART classique est basé sur le principe de la méthode CART de Breiman. Ici, nous utilisons un arbre de régression pour construire une typologie des points des trajectoires des vols en fonction des caractéristiques précédentes et de prévoir les instants de passage des aéronefs sur ces points. Le second modèle appelé CART modifié est une version améliorée du modèle précédent. Ce dernier est construit en remplaçant les prévisions calculées par l’estimation de la moyenne de la variable dépendante dans les nœuds terminaux du modèle CART classique par des nouvelles prévisions données par des régressions multiples à l’intérieur de ces nœuds. Ce nouveau modèle développé en utilisant l’algorithme de sélection et d’élimination des variables explicatives (Stepwise) est parcimonieux. En effet, pour chaque nœud terminal, il permet d’expliquer le temps de vol par des variables indépendantes les plus pertinentes pour ce nœud. Le troisième modèle est fondé sur la méthode MARS, modèle de régression multiple par les splines adaptatives. Outre la continuité de l’estimateur de la variable dépendante, ce modèle permet d’évaluer les effets directs des prédicteurs et de ceux de leurs interactions sur le temps de passage des aéronefs sur les points de leur trajectoire de vol prévue. Le quatrième modèle utilise la méthode d’échantillonnage bootstrap. Il s’agit notamment des forêts aléatoires où pour chaque échantillon bootstrap de l’échantillon de données initial, un modèle d’arbre de régression est construit, et la prévision du modèle général est obtenue par une agrégation des prévisions sur l’ensemble de ces arbres. Malgré le surapprentissage observé sur ce modèle, il est robuste et constitue une solution au problème d’instabilité des arbres de régression propre à la méthode CART. Les modèles ainsi construits ont été évalués et validés en utilisant les données test. Leur application au calcul des prévisions de la charge secteur en nombre d’avions entrants a montré qu’un horizon de prévision d’environ 20 minutes pour une fenêtre de temps supérieure à 20 minutes permettait d’obtenir les prévisions avec des erreurs relatives inférieures à 10%. Parmi ces modèles, CART classique et les forêts aléatoires présentaient de meilleures performances. Ainsi, pour l’autorité régulatrice des courants de trafic aérien, ces modèles constituent un outil d’aide pour la régulation et la planification de la charge des secteurs de l’espace aérien contrôlé. / In this thesis we propose probabilistic and statistic models based on multidimensional data for forecasting uncertainty on aircraft trajectories. Assuming that during the flight, aircraft follows his 3D trajectory contained into his initial flight plan, we used all characteristics of flight environment as predictors to explain the crossing time of aircraft at given points on their planned trajectory. These characteristics are: weather and atmospheric conditions, flight current parameters, information contained into the flight plans and the air traffic complexity. Typically, in this study, the dependent variable is difference between actual time observed during flight and planned time to cross trajectory planned points: this variable is called temporal difference. We built four models using method based on partitioning recursive of the sample. The first called classical CART is based on Breiman CART method. Here, we use regression trees to build points typology of aircraft trajectories based on previous characteristics and to forecast crossing time of aircrafts on these points. The second model called amended CART is the previous model improved. This latter is built by replacing forecasting estimated by the mean of dependent variable inside the terminal nodes of classical CART by new forecasting given by multiple regression inside these nodes. This new model developed using Stepwise algorithm is parcimonious because for each terminal node it permits to explain the flight time by the most relevant predictors inside the node. The third model is built based on MARS (Multivariate adaptive regression splines) method. Besides continuity of the dependent variable estimator, this model allows to assess the direct and interaction effects of the explanatory variables on the crossing time on flight trajectory points. The fourth model uses boostrap sampling method. It’s random forests where for each bootstrap sample from the initial data, a tree regression model is built like in CART method. The general model forecasting is obtained by aggregating forecasting on the set of trees. Despite the overfitting observed on this model, it is robust and constitutes a solution against instability problem concerning regression trees obtained from CART method. The models we built have been assessed and validated using data test. Their using to compute the sector load forecasting in term to aircraft count entering the sector shown that, the forecast time horizon about 20 minutes with the interval time larger than 20 minutes, allowed to obtain forecasting with relative errors less than 10%. Among all these models, classical CART and random forests are more powerful. Hence, for regulator authority these models can be a very good help for managing the sector load of the airspace controlled.