Master’s thesis is about indoor localization of objects or persons using Bluetooth Low Energy. The outcome of this work is determining the accuracy of a localization method One Nearest Zone on density of the localization network, implementing the Trilateration method and also determining the accuracy of this localization method on density of the localization network.

La thèse propose l’étude de systèmes de constellations locales permettant le positionnement dans des milieux difficiles. Dans ce contexte, il apparaît que les trajets indirects perturbent la mesure du temps de propagation entre l’antenne de l’émetteur et l’antenne du récepteur. L’éblouissement entre les signaux, phénomène d’interférence du CDMA, est exacerbé à cause des distances courtes et des variations de puissance de réception. La thèse apporte des réponses à ces deux problématiques. Pour les trajets indirects nous proposons d’utiliser les mesures de phase de la porteuse qui y sont moins sensibles. Il faut alors solutionner le problème de l’ambiguïté entière sur ces mesures. Une méthode le permet sans avoir recours à une technique différentielle en utilisant une boucle de poursuite insensible aux trajets indirects : la SMICL. Pour l’éblouissement, nous avons développé trois approches. L’une d’elle utilise les séquences de longueurs maximales et permet de réduire notablement son importance. Une deuxième méthode, baptisé Technique de la Double Emission (TDE), permet de supprimer intégralement ces interférences pour une paire d’émetteurs lorsque leurs Doppler respectifs sont égaux. Nous avons étudié le cas où les Doppler sont différents et mis au point une version améliorée de la TDE, la TDE étendue à la porteuse, qui permet de supprimer les influences du Doppler. Nous avons également montré que cette dernière peut s’appliquer à un émetteur fixe en présence d’une constellation de satellites. Une troisième méthode, appelée TDE maximale, utilise à nouveau une séquence de longueur maximale pour étendre la méthode TDE au cas de plusieurs émetteurs en présence. / The thesis proposes the study of systems of local constellations for positioning indoors.In this context, it appears that indirect paths disturb the measurement of time delay between the transmitter antenna and receiver antenna. The Near-Far problem between signals, a CDMA interference phenomenon, is exacerbated because of the short distances and variations in received power. The thesis provides answers to these two issues.For indirect multipath we propose to use carrier phase measurements. It must then solve the problem of ambiguity on these measurements. A method without carrying out a differential technique is proposed: a tracking loop insensitive to indirect routes: the SMICL. For the Near-Far problem, we have developed three approaches. One approach uses sequences of maximum length and significantly reduces its influence. A second method, called the Double Transmission Technique (DTT), can completely remove the interference for a pair of transmitters when their respective Doppler are equal. We have studied the case where different Doppler and developed an improved version of the DTT, the DTT extended to the carrier, which eliminates the influence of the Doppler. We also showed that this may also be applied to a fixed transmitter in the presence of a constellation of satellites. A third method, called DTT maximum, again uses a maximum length sequence to extend the method to the case DTT in the presence of several transmitters.

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Sensor Fusion is an essential framework in many Engineering fields. It is a relatively new paradigm for integrating data from multiple sources to synthesize new information that in general would not have been feasible from the individual parts. Within the wireless communications fields, many emerging technologies such as Wireless Sensor Networks (WSN), the Internet of Things (IoT), and spectrum sharing schemes, depend on large numbers of distributed nodes working collaboratively and sharing information. In addition, there is a huge proliferation of smartphones in the world with a growing set of cheap powerful embedded sensors. Smartphone sensors can collectively monitor a diverse range of human activities and the surrounding environment far beyond the scale of what was possible before. Wireless communications open up great opportunities for the application of sensor fusion techniques at multiple levels. In this dissertation, we identify two key problems in wireless communications that can greatly benefit from sensor fusion algorithms: Automatic Modulation Classification (AMC) and indoor localization and mapping based on smartphone sensors. Automatic Modulation Classification is a key technology in Cognitive Radio (CR) networks, spectrum sharing, and wireless military applications. Although extensively researched, performance of signal classification at a single node is largely bounded by channel conditions which can easily be unreliable. Applying sensor fusion techniques to the signal classification problem within a network of distributed nodes is presented as a means to overcome the detrimental channel effects faced by single nodes and provide more reliable classification performance. Indoor localization and mapping has gained increasing interest in recent years. Currently-deployed positioning techniques, such as the widely successful Global Positioning System (GPS), are optimized for outdoor operation. Providing indoor location estimates with high accuracy up to the room or suite level is an ongoing challenge. Recently, smartphone sensors, specially accelerometers and gyroscopes, provided attractive solutions to the indoor localization problem through Pedestrian Dead-Reckoning (PDR) frameworks, although still suffering from several challenges. Sensor fusion algorithms can be applied to provide new and efficient solutions to the indoor localization problem at two different levels: fusion of measurements from different sensors in a smartphone, and fusion of measurements from several smartphones within a collaborative framework. / Ph. D.

Received Signal Strength Indication (RSSI) fingerprinting is a popular technique in the fieldof indoor positioning. Many studies on the subject exist acknowledging Wi-Fi signal variationconnected to Wi-Fi signals, but does not discuss possible signal variation created byconnected devices nor consequential precision loss.Understanding more about the origins of signal variation in received signal strength indication(RSSI) fingerprinting would help deal with or prevent them as well as provide moreknowledge for applications based on such signals. Environments with a varying number ofconnected devices would benefit from knowing changes in localization precision resultingfrom the devices connecting and disconnecting from the access point because it wouldindicate whether workarounds for such circumstances would be necessary.To address this issue, the work presented here focuses on how the precision of RSSIfingerprinting vary given different levels of connected Wi-Fi devices. It was carried out byconducting real world experiments at times of low- and normal levels of connected devices toaccess points on two separate locations and evaluating precision changes between statedactivity levels. These experiments took place at the University of Borås as well as at Ericssonin Borås.Experimental findings indicate that the accuracy does deteriorate in higher levels of activitythan in low activity, even though not enough evidence to determine the precision ofdeterioration. The experiments thereby provide a foundation for location-based applicationsand services that can communicate the level of positional error that exist in differentenvironments which would make the users aware but also make the applications adaptaccordingly to different environments. Based on the precision achieved, we identify variousapplications that would benefit from our proposed model. These were applications that wouldtrack mobile resources, find immobile resources, find the movement flows of users as well asnavigation- and Wi-Fi coverage applications.Further research for investigating the exact correlation between access point stress andprecision loss is proposed to fully understand the implications connected devices have onRSSI fingerprinting.

Context: Wireless Sensor Networks (WSN) are applied to collect information by distributed sensor nodes (anchors) that are usually in fixed positions. Localization (estimating the location of objects) of moving sensors, devices or people which recognizes the location’s information of a moving object is one of the essential WSN services and main requirement. To find the location of a moving object, some of algorithms are based on RSSI (Received Signal Strength Indication). Since very accurate localization is not always feasible (cost, complexity and energy issues) requirement, RSSI-based method is a solution. This method has two specific features: it does not require extra hardware (cost and energy aspects) and theoretically RSSI is a function of distance. Objectives: In this thesis firstly, we develop an RSSI-based localization algorithm (server side application) to find the position of a moving object (target node) in different situations. These situations are defined in different experiments so that we observe and compare the results (finding accurate positioning). Secondly, since RSSI characteristic is highly related to the environment that an experiment is done in (moving, obstacles, temperature, humidity …) the importance and contribution of “environmental condition” in the empirical papers is studied. Methods: The first method which is a common LR (Literature Review) is carried out to find out general information about localization algorithms in (WSN) with focus on the RSSI-based method. This LR is based on papers and literature that are prepared by the collaborating company, the supervisor and also ad-hoc search in scientific IEEE database. By this method as well as relevant information, theoretical algorithm (mathematical function) and different effective parameters of the RSSI-based algorithm are defined. The second method is experimentation that is based on development of the mentioned algorithm (since experiment is usually performed in development, evaluation and problem solving research). Now, because we want to compare and evaluate results of the experiments with respect to environmental condition effect, the third method starts. The third method is SMS (Systematic mapping Study) that essentially focuses on the contribution of “environmental condition” effect in the empirical papers. Results: The results of 30 experiments and their analyses show a highly correlation between the RSSI values and environmental conditions. Also, the results of the experiments indicate that a direct signal path between a target node and anchors can improve the localization’s accuracy. Finally, the experiments’ results present that the target node’s antenna type has a clear effect on the RSSI values and in consequence distance measurement error. Our findings in the mapping study reveal that although there are a lot of studies about accuracy requirement in the context of the RSSI-based localization, there is a lack of research on the other localization requirements such as performance, reliability and stability. Also, there are a few studies which considered the RSSI localization in a real world condition. Conclusion: This thesis studies various localization methods and techniques in WSNs. Then, the thesis focuses on the RSSI-based localization by implementing one algorithm and analyzing the experiments’ results. In our experiments, we mostly focus on environmental parameters that affect localization’s accuracy. Moreover, we indicate some areas of research in this context which need more studies.

Avec la demande croissante d'espace urbain, de plus en plus de parkings à plusieurs étages sont nécessaires. Bien que ces parkings contribuent à une utilisation plus efficace de l'espace urbain, ils introduisent également un nouveau problème. Les rapports suggèrent environ 70 millions d'heures de recherche d'emplacements de stationnement chaque année, soit une perte équivalente de 700 millions d'euros pour la seule France. En outre, les utilisations des parkings vont au-delà de leurs objectifs initiaux. Des fonctionnalités exigeantes telles que le chargeur électrique, la réservation en ligne de places de stationnement, le guidage dynamique ou le paiement mobile, etc. transforment un parking en un environnement intelligent et compétitif. Une solution à ce problème consiste à développer un système de navigation autonome pour les véhicules intelligents en situation de parking. La thèse identifiera une de ces sous-tâches, à savoir la localisation dans des environnements non autorisés par GPS. Cette thèse présentera une nouvelle méthode pour résoudre le problème indiqué tout en maintenant le système en respectant quatre critères: disponibilité, évolutivité, universalité et précision. Il y a deux étapes principales: (1) une solution permettant de reproduire le comportement du GPS pour un environnement refusé par GPS, et (2) un cadre permettant la fusion de systèmes de type GPS avec d'autres méthodes de localisation pour obtenir une précision de localisation élevée. Tout d'abord, un système de localisation Wi-Fi Fingerprinting est utilisé. Une approche utilisant un réseau de neurones d'ensemble sur une base de données d'empreintes hybrides Wi-Fi est proposée dans cette thèse. Des expériences menées sur une durée d'un an montrent que ce système est capable de localiser des véhicules présentant une erreur moyenne de 2,25 m dans le repère global (WGS84). Deuxièmement, une solution de localisation complète doit être une fusion de plusieurs techniques. Cela permet aux niveaux de localisation global et local de fonctionner ensemble. Parallèlement, la redondance dans le système améliore la précision et la fiabilité. Dans cette thèse, un cadre de fusion flexible pour plusieurs capteurs de localisation est proposé. Ce cadre de fusion traitera non seulement de l'environnement refusé par le GPS, mais pourrait également être utilisé dans l'environnement assisté par GPS et assurer une transition en douceur entre les deux zones. Pour accomplir cette tâche exigeante, un filtre à particules modèle de mélange gaussien est développé. Alors que le modèle de mouvement de ce filtre à particules intègre des données provenant de l'IMU (unité de mesure inertielle) ou du laser-SLAM, le modèle de correction est un modèle de mélange gaussien de plusieurs observations obtenues à partir du système de localisation d'empreintes digitales Wi-Fi. Avec deux véhicules intelligents (une Cybercar et une Citroen C1), 64 expériences ont été réalisées pour valider le cadre. Une erreur de localisation moyenne de 0,5 m est obtenue dans un cadre de coordonnées global. Comparez avec d'autres solutions avec une erreur de localisation moyenne de 0,2 m dans les cadres de coordonnées locales; Cette solution proposée présente également des avantages en termes d'évolutivité, de disponibilité et d'universalité. / With the increasing demand for urban space, more and more multistory carparks are needed. Although these carparks help to utilize urban space more efficient, they also introduce a new problem. Reports suggest approximately 70 million hours of parking slot searching each year, equivalently 700 million euros loss for France alone. In addition, carparks uses are exceeding their original purposes. Demanding features such as electric charger, online booking of parking spaces, dynamic guidance or mobile payment etc. turn a carpark into a competitive smart environment. One solution to this problem is to develop an autonomous navigation system for intelligent vehicles in the carpark situation. The thesis will identify one of these sub-tasks namely localization in GPS-denied environments. This thesis will present a novel method to solve the indicated problem while keeping the system follows four criteria: availability, scalability, universality and accuracy. There are two main steps: (1) a solution to replicate the GPS behaviour for the GPS-denied environment, and (2) a framework that allows the fusion of GPS-like systems with other localization methods to achieve a high localization accuracy. First, a Wi-Fi Fingerprinting localization system is employed. An approach using an ensemble neural network on a hybrid Wi-Fi fingerprinting database is proposed in this thesis. Experiments in a year-long duration show that this system is capable of localizing vehicles with 2.25m of mean error in the global coordinate frame (WGS84). Second, a complete localization solution must be a fusion of multiple techniques. This allows global as well as local levels of localization to function together. At the same time, having redundancy in the system boosts accuracy and reliability. In this thesis, a flexible fusion framework for multiple localization sensors is proposed. This fusion framework will not only deal with the GPS-denied environment but could be potentially used in the GPS-aided environment and provide a smooth transition between the two areas. To accomplish this demanding task, a Gaussian Mixture Model Particle Filter is developed. While the motion model of this particle filter incorporates data from the IMU (Inertial Measurement Unit) or laser-SLAM, the correction model is a Gaussian mixture model of multiple observations obtained from the Wi-Fi fingerprinting localization system. With two intelligent vehicles (a Cybercar and a Citroen C1 car), 64 experiments were carried out to validate the framework. A mean localization error of 0.5m is achieved in a global coordinate frame. Compare to other solutions with 0.2m of mean localization error in local coordinate frames; this proposed solution has advantages in terms of scalability, availability and universality as well.

This diploma thesis focuses on possible utilization of LoRa (Long Range) technology for indoor localization purposes. In this thesis, the starter kit SK-iM282A is used to create a LoRa-based wireless link in the 2.4 GHz ISM band. Main attention is focused on the experimental localization using the RSSI method in the various transmission environments, including a description of the localization principle, the procedure of processing the measured data and the evaluation of localized coordinates. The rightness of the proposed measurement setup and methodology is verified by extensive measurements in various environments and compared with state-of-the-art article.

L’objet de cette thèse est l’étude de la localisation et de la navigation à l’intérieur de bâtiments à l’aide des signaux disponibles dans les systèmes mobiles cellulaires et, en particulier, les signaux GSM.Le système GPS est aujourd’hui couramment utilisé en extérieur pour déterminer la position d’un objet, mais les signaux GPS ne sont pas adaptés à la localisation en intérieurIci, la localisation en intérieur est obtenue à partir de la technique des «empreintes» de puissance des signaux reçus sur les canaux utilisés par les réseaux GSM. Elle est réalisée à l’échelle de la pièce. Une classification est effectuée à partir de machines à vecteurs supports et les descripteurs utilisés sont les puissances de toutes les porteuses GSM. D’autres capteurs physiques disponibles dans les téléphones portables fournissent des informations utiles pour déterminer la position ou le déplacement de l’utilisateur. Celles-ci, ainsi que la cartographie de l’environnement, sont associées aux résultats obtenus à partir des «empreintes» GSM au sein de filtres particulaires afin d’obtenir une localisation plus précise, et sous forme de coordonnées continues.Les résultats obtenus montrent que l’utilisation des seules empreintes GSM permet de déterminer la pièce correcte dans 94% des cas sur une durée courte et que les performances restent stables pendant plusieurs mois, de l’ordre de 80%, si les données d’apprentissage sont enregistrées sur quelques jours. L’association de la cartographie du lieu et des informations issues des autres capteurs aux données de classification permettent d’obtenir les coordonnées de la trajectoire du système mobile avec une bonne précision et une bonne fiabilité. / GPS has long been used for accurate and reliable outdoor localization, but it cannot operate in indoor environments, which suggests developing indoor localization methods that can provide seamless and ubiquitous services for mobile users.In this thesis, indoor localization is realized making use of received signal strength fingerprinting technique based on the existing GSM networks. A room is defined as the minimum location unit, and support vector machine are used as a mean to discriminate the rooms by classifying received signal strengths from very large number of GSM carriers. At the same time, multiple sensors, such as accelerometer and gyroscope, are widely available for modern mobile devices, which provide additional information that helps location determination. The hybrid approach that combines the GSM fingerprinting results with mobile sensor and building layout information using a particle filter provides a more accurate and fine-grained localization result.The results of experiments under realistic conditions demonstrate that correct room number can be obtained 94% of the time provided the derived model is used before significant received signal strength drift sets in. Furthermore, if the training data is sampled over a few days, the performance can remain stable exceeding 80% over a period of months, and can be further improved with various post-processing techniques. Moreover, including the mobile sensors allows the system to localize the mobile trajectory coordinates with high accuracy and reliability.

Récemment, la localisation statique et dynamique d'un objet ou d'une personne est devenue l'un des plus importantes fonctionnalités d'un système de communication, du fait de ses multiples applications. En effet, connaître la position d'un terminal mobile (MT), en milieu extérieur ou intérieur, est généralement d'une importance majeure pour des applications fournissant des services basés sur la localisation. Ce développement des systèmes de localisation est dû au faible coût des infrastructures de réseau sans fil en milieu intérieur (WLAN). Les techniques permettant de localiser des MTs diffèrent selon les paramètres extraits des signaux radiofréquences émis entre des stations de base (BSs) et des MTs. Les conditions idéales pour effectuer des mesures sont des environnements dépourvus de tout obstacle, permettant des émissions directes entre BS et MT. Ce n'est pas le cas en milieu intérieur, du fait de la présence continuelle d'obstacles dans l'espace, qui dispersent les rayonnements. Les mesures prises dans ces conditions (NLOS, pour Non Line of Sight) sont imprévisibles et diffèrent de celles prises en condition LOS. Afin de réduire les erreurs de mesure, différentes techniques peuvent être utilisées, comme la mitigation, l'approximation, la correction à priori, ou le filtrage. En effet, l'application de systèmes de suivi (TSs) constitue une base substantielle pour la navigation individuelle, les réseaux sociaux, la gestion du trafic, la gestion des ressources mobiles, etc. Différentes techniques sont appliquées pour construire des TSs en milieu intérieur, où le signal est bruité, faible voire inexistant. Bien que les systèmes de localisation globaux (GPS) et les travaux qui en découlent fonctionnent bien hors des bâtiments et dans des canyons urbains, le suivi d'utilisateurs en milieu intérieur est bien plus problématique. De ce fait, le problème de prédiction reste un obstacle essentiel à la construction de TSs fiable dans de tels environnements. Une étape de prédiction est inévitable, en particulier, dans le cas où l'on manque d'informations. De multiples approches ont été proposées dans la littérature, la plupart étant basées sur un filtre linéaire (LF), un filtre de Kalman (KF) et ses variantes, ou sur un filtre particulaire (PF). Les filtres de prédiction sont souvent utilisés dans des problèmes d'estimation et l'application de la dérivation non entière peut limiter l'impact de la perte de performances. Ce travail présente une nouvelle approche pour la localisation intérieure par WLAN utilisant un groupement des coordonnées. Ensuite, une étude comparative des techniques déterministes et des techniques d'apprentissage pour la localisation intérieure est présentée. Enfin, une nouvelle approche souple pour les systèmes de suivi en milieu intérieur, par application de la dérivation non entière, est présentée / Recently, the static and dynamic geo-location of a device or a person has become one of the most important aspects of communication systems because of its multiple applications. In general, knowing the position of a mobile terminal (MT) in outdoor or indoor environments is of major importance for applications providing services based on the location. The development of localization systems has been mainly driven by the avail- ability of the affordable cost of indoor wireless local area network (WLAN) infrastructure. There exist different techniques to localize MTs with the different mainly depending on the type of the metrics extracted from the radio frequency signals communicated between base stations (BSs) and MTs. Ideal measurements are taken in environments which are free of obstacles and in direct ray tracings between BS and MT. This is not the case in indoor environment because the daily use of permanent obstacles in the work space scatters the ray tracings. Measurements taken in Non Line Of Sight (NLOS) are unpredictable and different from those taken in LOS. In order to reduce measurement errors, one can apply different techniques such as mitigation, approximation, prior correction, or filtering. Tracking systems (TSs) have many concrete applications in the space of individual navigation, social net- working, asset management, traffic management, mobile resource management, etc. Different techniques are applied to build TSs in indoor environments, where the signal is noisy, weak or even non-existent. While the Global Positioning System (GPS) devices work well outside buildings and in urban canyons, tracking an indoor user in a real-world environment is much more problematic. The prediction problem remains an essential obstacle to construct reliable indoor TSs. Then lacks of reliable wireless signals represent the main issue for indoor geo-location systems. This obviously calls for some sort of predictions and corrections to overcome signal reliability, which unavoidably open the door for a multitude of challenges. Varieties of approaches were proposed in the literature. The most used are the ones based on prediction filters, such as Linear Filter (LF), Kalman Filter (KF) and its derivatives, and Particle Filters (PF). Prediction filters are often used in estimation problems and applying Digital Fractional Differentiation can limit the impact of performance degradations. This work presents a novel approach for the WLAN indoor geo-location by using coordinates clustering. This approach allows overcoming the limitations of NLOS methods without applying any of mitigation, approximation, prior correction, or filtering approaches. Then a comparison study of deterministic and learning techniques for indoor geo-location is presented. Finally, it presents a novel soft approach for indoor tracking system by applying digital fractional integration (DFI) to classical prediction filters