La RFID (Radio Frequency IDentification) est une technologie qui permet la communication entre un tag et un lecteur par ondes radios. Un tag est dit passif lorsqu’il utilise l’onde fournie par le lecteur pour transmettre une information à celui-ci. Cette technologie couvre aujourd’hui tous les domaines de l’industrie. D’après les statistiques, 13% des applications industrielles utilisant cette technologie sont liées au domaine du transport. Dans notre cas, l’objectif a été d’étudier théoriquement et expérimentalement le comportement des tags UHF RFID passifs dans les véhicules de transport terrestres. L’application visée est un système de maintenance de véhicule de transport terrestre intelligent capable de rendre compte de l’état de santé du véhicule lors de son retour sur une zone de dépôt. L’utilisation d’un seul lecteur RFID UHF situé à l’extérieur des véhicules à contrôler est envisagé afin de minimiser le coût de revient final du système. L’étude théorique a été réalisée à partir d’un logiciel de simulation développé en interne (SIMUEM3D) qui est basé sur ce que nous avons appelé "l’Optique Géométrique Modifiée (OGM)". Des mesures d’atténuation d’un signal sinusoïdal pur (CW) ainsi que de taux de lecture de tags RFID UHF ont été réalisées dans deux types de véhicules : un fourgon tôlé et une berline de tourisme. Le modèle numérique a été étalonné grâce à ces mesures. Des études paramétriques portant sur la position du lecteur, la vitesse du véhicule et la diversité spatiale ont été conduites. Des recommandations d’installation sont fournies pour permettre de collecter des informations de maintenance et de détection à l’intérieur de véhicules de transport terrestres. / The RFID (Radio Frequency Identification) is a technology which allows communication between a tag and a reader by using radio waves. A tag is called “passive” when it uses the received energy supplied by the reader without using any battery. Many industrial domains are covered by this technology. From statistical datas, 13% of industrial applications belong to the transportation domain. In our case, the objective is to study theoretically and experimentally the behavior of passive UHF RFID tags located inside terrestrial vehicles. The targeted application is an intelligent maintenance and repair system which is able to provide the health of a vehicle that returns back on a storage area. The choice has been made to use only one UHF RFDI reader fixed outside of the monitored vehicles in order to minimize the total cost of the system. The theoretical study has been performed using a modelling tool developed internally (SIMUEM3D) and based on what we call the “Modified Geometrical Optics (MGO)”. Measurements of the attenuation of a sinusoidal signal (CW) and of the reading rate have been carried out in two kinds of vehicles : a cargo van and a passenger car. The numerical model has been calibrated with those measurements. Parametrical studies have been done dealing on the reader location, the vehicle speed and spatial diversity. Installation recommendations are given in order to guide the use of such a maintenance and detections system inside utility cars, tourism cars or even buses.

With the rapid development of wireless networking technology and the great growth of the service demand, accurate location estimation of a mobile station (MS) in an indoor wireless system has gained considerable attention. Since most wireless communication systems used for indoor position location may suffer from dense NLOS propagation error, which leads to a severe degradation of position accuracy. In this thesis, we propose a hybrid TOA/RSSI wireless positioning technique for indoor UWB systems to gain favorable position accuracy, by using the fine resolution of UWB signals, geometrical feature of cell layout, the path loss model and the received signal strength, based on the time of arrival (TOA) range measurements. The algorithm induces the objective function from the geometrical relationships of the base stations (BSs) and TOA range circles, and utilizes the received signal strength and pre-estimated path loss model, which should be well approximating the propagation conditions, to discriminate between LOS or NLOS range measurements and define the weight factors used to describe how credible TOA range measurements are and take effect on the objective function during the location calculating process. Simulation results show that the positioning accuracy of the hybrid TOA/RSSI method is much higher than that of other positioning methods under different distributed NLOS errors.

In the thesis, we propose a non-line of sight (NLOS) mitigation approach based on the interacting multiple model (IMM) algorithm. The IMM-based structure, composed of a biased Kalman filter (BKF) and a Kalman filter with NLOS-discarding process (KF-D), is capable of mitigating the ranging error caused by the NLOS effects, and therefore improving the performance and accuracy in wireless location systems. The NLOS effect on signal transmission is one of the major factors that affect the accuracy of the time-based location systems. Effective NLOS identification and mitigation usually count on pre-determined statistic distribution and hypothesis assumption in the signals. Because the variance of the NLOS error is much large than that of measurement noise, hypothesis testing on the LOS/NLOS status can be formulated.The BKF combines the sliding window and decides the status by using hypothesis testing. The calculated variance and the detection result are used in switching between the biased and unbiased modes in the Kalman filter. In the contrast, the KF-D scheme identifies the NLOS status and tries to eliminate the NLOS effects by directly using the estimated results from the LOS stage. The KF-D scheme can achieve reasonably good NLOS mitigation if the estimates in the LOS status are obtained. Due to the discarding process, changes of the state vector within the NLOS stage are possibly ignored, and will cause larger errors in the state estimates. The BKF and KF-D can make up for each other by formulating the filters in an IMM structure, which could tune up the probabilities of BKF and KF-D. In our approach, the measured data are smoothed by sliding window and a BKF. The variance of data and the hypothesis test result are passed to the two filters. The BKF switches between the biased/unbiased modes by using the result. The KF-D may receive the estimated value from BKF based on the results. The probability computation unit changes the weights to get the estimated TOA values. With the simulations in ultra-wideband (UWB) signals, it can be seen that the proposed IMM-based approach can effectively mitigate the NLOS effects and increase the accuracy in wireless position.

With the rapid development of wireless networking technology and the great growth of service demand, accurate wireless location estimation has gained considerable attention. Most wireless location system may suffer from non-line-of-sight (NLOS) propagation error, which leads to a severe degradation of position accuracy. In this thesis, we propose a hybrid TOA/AOA (time of arrival/ angle of arrival) non-line-of-sight identification and wireless location technology to cope with NLOS condition. This algorithm can simultaneously determine the number of line-of-sight (LOS) base stations (BSs) and identify them. The identification part is to collect all TOA and AOA parameters from all BSs and to use residual information to detetmine whether the NLOS error is present in measurements. The localization method only processes the LOS measurements to avoid the NLOS error and increases position accurary. The simulation results show that the location system with TOA measurements can identify three or more LOS-BSs. The system has a high identification accuracy when the number of LOS-BSs is more than three. When the number of LOS-BSs is three, the degraded identification capability leads to larger position errors. When the AOA information is available in the positioning system, the TOA is combined with AOA because the property of the AOA localization method which needs only two measurements to locate the MS makes the location system capable of identifying two LOS-BSs. When the number of LOS-BSs is two, the combination of TOA and AOA measurements maintains a higher NLOS identification accuracy and make its location performance remarkably promoted.

Specialists are expecting the knowledge of location will trigger yet another revolution in mobile services. Location-base services (LBS) have attracted many researchers and enterprises and one of the key aspects of LBS is positioning technology. Considering a wider and more complex field - ubiquitous computing, location is the fundamental element. This work focuses on some aspects of the new techniques of terrestrial positioning systems. Wireless LAN is one of the most popular systems used for positioning for indoor environments and public places. We have investigated the trilateration and fingerprinting approaches and the results showed the advantages of fingerprinting. A novel method to generate the fingerprints database based on Universal Kriging (UK) was developed, which can not only significantly decrease the training time, but also increase the accuracy of estimates. In mobile phone positioning systems, most techniques suffer from the non-line-of-sight (NLOS) propagation. We investigated the specifics of NLOS error, and proposed a method to mitigate the errors. Furthermore, a new algorithm named WSMM (wireless signal map matching) was discussed. Simulations and experiments verified the idea, and the accuracy of positioning can be improved greatly. Since fingerprinting technique can utilize rather than suffer from the NLOS propagation, it was also applied in mobile phone positioning system. Experiments showed both the deterministic approach and probabilistic approach can provide better results comparing with other techniques in suburban area. To achieve a robust positioning system and provide more useful information of the user, multisensor combination and data fusion are necessary. As the first step of future research, a mulitsensor synchronization system was developed. This system can promisingly achieve synchronization with error less than 0.4 ms, which is suitable for most land applications. Hence the main findings of this thesis are: (1) a novel method of yielding fingerprint database for both wireless LAN (WLAN) and mobile phone systems when using the fingerprinting technique for positioning; (2) a database method to mitigate NLOS error for mobile phone positioning systems; (3) a low cost synchronization system for integration of multiple sensors.

The major problems encountered in wireless location are the effects caused by non-line of sight (NLOS) propagation and multipath interference. In the thesis, we propose an approach to mitigate NLOS error. First of all, we use improved biased Kalman filter (IBKF) based on time of arrival (TOA) measurement to identify and mitigate NLOS error. Applying the statistic characteristic that the standard deviation of the NLOS propagation errors is generally much larger than that of measurement noises in the LOS condition, we combine hypothesis test and sliding window to identify NLOS error. According to the feedback identification and the calculated standard deviation, IBKF switches biased or unbiased to process TOA measurement. Nevertheless, the performance of IBKF-TOA is still affected slightly by NLOS error. Since extended Kalman filter (EKF) based on received signal strength (RSS) measurement is designed for prespecified environments, the effect of NLOS mitigation is more obvious. Moreover, EKF-RSS not only exists higher error probability in NLOS identification, but also needs longer time to converge in the cases that start with NLOS. Comparing IBKF-TOA with EKF-RSS, we adopt interacting multiple model (IMM) in the proposed data fusion structure for processing TOA and RSS measurements. In the proposed scheme, we reserve the basic IMM structure and add the step of NLOS identification into basic IMM structure. By accurate NLOS identification results and soft decision of IMM, the proposed scheme will switch to adequate filter mode and obtain better estimation. With simulation in UWB channel, the analysis and performance evaluation show advantages and disadvantages of using IBKF-TOA, EKF-RSS, and proposed scheme. Simulation results reveal that NLOS error can be mitigated effectively by data fusion of TOA and RSS measurements and can achieve high accuracy in positioning and tracking.

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Position location has become one of the more important tasks for improving communication and networking performance for future commercial wireless systems. It is also the enabling technology for many control and sensing applications envisioned by the wireless sensor networks (WSN). Despite its meaningfulness and many algorithms being developed in the past several years, position location in harsh propagation environments remains to be a challenging issue, due mainly to the lack of sufficient infrastructure support and the prominent phenomenon of non-line-of-sight (NLOS) signal propagation. Recently, adopting the concept of collaborative position location has attracted much research interest due to its potential in overcoming the abovementioned two difficulties. In this work, we approach collaborative position location from two different angles. Specifically, we investigate the optimal performance of collaborative position location, which serves as a theoretical performance benchmark. In addition, we developed a computationally efficient algorithm for collaborative position location and incorporated an effective NLOS mitigation method to improve its performance in NLOS-dense environments. Overall, our work provides insight into both theoretical and practical aspects of collaborative position location. / Ph. D.

"Precise Tracking of Things (PToT) using RF signals has posed a serious challenge in an indoor environment. The precision localization information is an enabler for better coordinated-tasks and is essential for a successful launch of many emerging applications. PToT relies on two principal components, a novel navigation (tracking) algorithm, and a hybrid 3D fingerprint database. In this dissertation, we begin by using the two widely known ranging techniques, Time Of Arrival (TOA) associated with Ultra-wideband (UWB) and Received Signal Strength (RSS) with WiFi signals. First, we use the theoretical models derived from empirical measurement of TOA and RSS to analyze the performance of hybrid (WiFi & UWB) cooperative localization accuracy in a multi-robot operation in a typical office environment. To measure the performance of this hybrid localization, we derive a mathematical formulation for the Crame ́r-Rao-Lower- Bound (CRLB). The hybrid method shows more accuracy over WiFi-only approach. In achieving more precision, we extend our work. Second, we introduce a novel approach, a Kernel Method Particle Filter (KMPF) for tracking and predicting the position by accessing the information created by hybrid 3D fingerprint database. We derive the mathematical and statistical framework for the Particle Filter based on the statistical assumptions about the behavior of channel models. We also describe the formation of one of the necessary PToT component, a 3D fingerprint database. We compare the performance of the KMPF against the CRLB using WiFi signal channel models."

For the mass demands of personalized security services, such as tracking, supervision, and emergent rescue, the location technologies of mobile communication have drawn much attention of the governments, academia, and industries around the world. However, existing location methods cannot satisfy the requirements of low cost and high accuracy. We hypothesized that a new mobile location algorithm based on the current GSM system will effectively improve user satisfaction. In this study, a prototype system will be developed, implemented, and experimented by integrating the useful information such as the geometry of the cell layout, and the related mobile positioning technologies. The intersection of the regions formed by the communication space of the base stations will be explored. Furthermore, the density-based clustering algorithm (DCA) and GA-based algorithm will be designed to analyze the intersection region and estimate the most possible location of a mobile phone. Simulation results show that the location error of the GA-based is less than 0.075 km for 67% of the time, and less than 0.15 km for 95% of the time. The results of the experiments satisfy the location accuracy demand of E-911.