This thesis presents mobile positioning and tracking with non-line of sight (NLOS) mitigation using time difference of arrival (TDOA) in biased extended Kalman filter (BEKF) in indoor dense multipath Ultra-Wideband (UWB) environment. The most serious issues which render to influence accuracy for the time-based location system is NLOS problem. Kalman filters (KFs) are used for smoothing range measurement data, and a method with sliding window is proposed to process range data for calculating standard deviation in a hypothesis testing and then identifying NLOS scenarios. When the measured arrival time has been converted to range difference, the biased extended Kalman filter is proposed to mitigate the NLOS error in the certain base stations (BSs) for mobile station (MS) positioning and trajectory tracking. From the simulation results in the indoor positioning environment with measurement and NLOS error, the sliding window algorithm and biased extended Kalman filter have higher accuracy than other related methods for NLOS identification and mitigation in positioning.

Accurate indoor geolocation is an important and novel emerging technology for commercial, public safety, and military applications. Since most wireless communication systems used for indoor position location may suffer from dense multipath situation, which leads to a severe degradation of position accuracy. The improved TDOA/AOA(Time Difference of Arrival/ Angle of Arrival) position location for indoor ultra-wide band (UWB) systems in the thesis improves the position accuracy of indoor location by using fine resolution of UWB signals. In the line of sight situation, by means of increasing angle of arrival (AOA) information to time difference of arrival (TDOA) based location to achieve the goal of accurate indoor geolocation and provides non-line of sight (NLOS) error mitigation for time measurement and AOA selection to suppress the impact to position accuracy in NLOS environment. Finally, the extended Kalman filter is used to perform position tracking of the target. In the simulations, the NLOS error in time measurement is produced according to the characteristics of indoor UWB channel. Several assumptions of NLOS errors are made in angular measurement. It is observed that proposed method efficiently mitigates the position error in NLOS environment, and detect if the NLOS exists between base station and mobile station immediately.

For the mass demands of wireless communication services, the mobile location technologies have drawn much attention of the governments, academia, and industries around the world. In wireless communication, one of the main problems with accurate location is nonline of sight (NLoS) propagation. To solve the problem, we present a new location algorithm with clustering technology by utilizing the geometrical feature of cell layout, time of arrival (ToA) range measurements, and three base stations. The mobile location is estimated by solving the optimal solution of the objective function based on the high density cluster. Simulations study was conducted to evaluate the performance of the algorithm for different NLoS error distributions and various upper bound of NLoS error. The results of our experiments demonstrate that the proposed algorithm is significantly more effective in location accuracy than range scaling algorithm, linear lines of position algorithm, and Taylor series algorithm, and also satisfies the location accuracy demand of E-911.

The technique of mobile location has become a popular research topic since the number of related applications for the location information is growing rapidly. The decision to make the location of mobile phones under the U.S. Federal Communications Commission (FCC) in 1996 is one of the driving forces to research and provide solutions to it. But, in wireless communication systems, non line of sight (NLOS) propagation is a key and difficult issue to improve mobile location estimation. We propose an efficient location algorithm which can mitigate the influence of NLOS error. First, based on the geometric relationship between known positions of the base stations, the theorem of ¡§Fermat Point¡¨ is utilized to collect the candidate positions (CPs) of the mobile station. Then, a set of weighting parameters are computed using a density-based clustering method. Finally, the location of mobile station is estimated by solving the optimal solution of the weighted objective function. Different distributions of NLOS error models are used to evaluate the performance of this method. Simulation results show that the performance of the least range measure (LRM) algorithm is slightly better than density-based clustering algorithm (DCA), and superior to the range based linear lines of position algorithm (LLOP) and range scaling algorithm (RSA) on location accuracy under different NLOS environments. The simulation results also satisfy the location accuracy demand of Enhanced 911 (E-911).

In wireless location systems, received signals may be influenced by non-line of sight (NLOS) propagation errors, which yield severe degradation of location accuracy.Therefore, to distinguish how many measurement signals are line-of-sight (LOS) and to identify them simultaneously will contribute to the increase of location accuracy.We propose a method based on recursive hypothesis testing algorithm, and use residual information to determine whether the NLOS errors are present in measurements. Since the probability distribution of measurements with NLOS errors is different from that of measurements without NLOS errors, a likelihood ratio test can be used in determining the LOS/NLOS status of the measurements. To search for an optimal threshold for the hypothesis testing, particle filtering optimization(PFO) is adopted. The PFO algorithm uses particle filtering to find the best threshold for determining the status of signals measured at all base stations (BSs). In the PFO algorithm, the clustering property of K-means is also used in separating particles, thereby the search of optimal threshold may be implemented in parallel.In this thesis, we focus on the hybrid TOA/AOA (time of arrical/angle of arrival) location method, in which localization only uses the LOS location measurements to calculate the location of a mobile station. Simulation results show that the proposed algorithm performs better than other algorithms which suffer from different degrees of NLOS errors. The proposed scheme also obtains higher identification rate of LOS-BSs in different situations by using the optimal thresholds for status detection.

The use of ubiquitous network has made real time tracking of objects, animals and human beings easy through the use of radio frequency identification system (RFID). Localization techniques in RFID rely on accurate estimation of the read range between the reader and the tags. The tags consist of a small chip and a printed antenna which receives from and transmits information to the reader. The range information about the distance between the tag and the reader is obtained from the received signal strength indication (RSSI). Accuracy of the read range using RSSI can be very complicated especially in complicated propagation environment due to the nature and features of the environment. There are different kinds of localisation systems and they are Global Positioning System (GPS) which can be used for accurate outdoor localization; while technologies like artificial vision, ultrasonic signals, infrared and radio frequency signals can be employed for indoor localization. This project focuses on the location estimation in RFID Non Line-of-Sight (NLOS) environment using Real Time Localization System (RTLS) with passive tags, in carrying out passengers and baggage tracking at the airport. Indoor location radio sensing suffers from reflection, refraction and diffractions due to the nature of the environment. This unfavourable phenomenon called multipath leads to delay in the arrival of signal and the strength of signal received by receiving antenna within the propagation channel which in turns affects the RSSI, yielding inaccurate location estimation. RTLS based on time difference of arrival and error compensation technique and extended Kalman filter technique were employed in a NLOS environment to determine the location of tag. The better method for location estimation in a NLOS between the Kalman filtering and extended Kalman filtering is investigated. According to simulation results, the extended Kalman filtering technique is more suitable to be applied to RTLS.

Abstract The multiple–input multiple–output (MIMO) technique, applying several transmit and receive antennas in wireless communications, has emerged as one of the most prominent technical breakthroughs of the last decade. Wideband MIMO parameter estimation and its applications to the MIMO orthogonal frequency division multiplexing (MIMO–OFDM) channel estimation and mobile positioning are studied in this thesis. Two practical MIMO channel models, i.e., correlated-receive independent-transmit channel and correlated-transmit-receive channel, and associated space-time parameter estimation algorithms are considered. Thanks to the specified structure of the proposed training signals for multiple transmit antennas, the iterative quadrature maximum likelihood (IQML) algorithm is applied to estimate the time delay and spatial signature for the correlated-receive independent-transmit MIMO channels. For the correlated-transmit-receive MIMO channels, the spatial signature matrix corresponding to a time delay can be further decomposed in such a way that the angle of arrival (AOA) and the angle of departure (AOD) can be estimated simultaneously by the 2-D unitary ESPRIT algorithm. Therefore, the combination of the IQML algorithm and the 2-D unitary ESPRIT algorithm provides a novel solution to jointly estimate the time delay, the AOA and the AOD for the correlated-transmit-receive MIMO channels. It is demonstrated from the numerical examples that the proposed algorithms can obtain good performance at a reasonable cost. Considering the correlated-receive independent-transmit MIMO channels, channel coefficient estimation for the MIMO–OFDM system is studied. Based on the parameters of the correlated-receive independent-transmit MIMO channels, the channel statistics in terms of the correlation matrix are developed. By virtue of the derived channel statistics, a joint spatial-temporal (JST) filtering based MMSE channel estimator is proposed which takes full advantage of the channel correlation properties. The mean square error (MSE) of the proposed channel estimator is analyzed, and its performance is also demonstrated by Monte Carlo computer simulations. It is shown that the proposed JST minimum mean square error (MMSE) channel estimator outperforms the more conventional temporal MMSE channel estimator in terms of the MSE when the signals in the receive antenna array elements are significantly correlated. The closed form bit error probability of the space-time block coded OFDM system with correlation at the receiver is also developed by taking the channel estimation errors and channel statistics, i.e., correlation at the receiver, into account. Mobile positioning in the non-line of sight (NLOS) scenarios is studied. With the knowledge of the time delay, the AOA and the AOD associated with each NLOS propagation path, a novel geometric approach is proposed to calculate the MS's position by only exploiting two NLOS paths. On top of this, the least squares and the maximum likelihood (ML) algorithms are developed to utilize multiple NLOS paths to improve the positioning accuracy. Moreover, the ML algorithm is able to estimate the scatterers' positions as well as those of the MSs. The Cramer-Rao lower bound related to the position estimation in the NLOS scenarios is derived. It is shown both analytically and through computer simulations that the proposed algorithms are able to estimate the mobile position only by employing the NLOS paths.

The Federal Communications Commission (FCC) that regulates all wireless communication service providers has issued modified regulations that all service providers must select a method for providing position location (PL) information of a user, requesting for E-911 service, by October 2000. The wireless 911 rules adopted by the FCC are aimed both for improving the reliability of the wireless 911 services and for providing the enhanced features generally available for wireline calls. From the service providers' perspective, effective position location technologies must be utilized to meet the FCC rules. The Time-of-Arrival (TOA) and the Time-Difference-of-Arrival (TDOA) methods are the technology that can provide accurate PL information without necessitating excessive hardware or software changes to the existing cellular/PCS infrastructure. The TOA method works well when the mobile station (MS) is located close to the controlling base station. With certain corrections applied, the TOA method can perform reliably even in the presence of Non-Line-of-Sight (NLOS) condition. The TDOA method performs better when the MS is located at a significant distance from the controlling base station. However, under the NLOS environmental condition, the performance of the TDOA method degenerates significantly. The fusion of TOA and the TDOA method exhibits certain advantages that are not evident when only one of the methods is applied. This thesis investigates the performance of data fusion techniques for a PL system, that are able to merge independent estimates obtained from TOA and TDOA measurements. A channel model is formulated for evaluating PL techniques within a NLOS cellular environment. It is shown that NLOS propagation can introduce a bias into TDOA measurements. A correction method is proposed for removing this bias and new corrected data fusion techniques are compared with previous techniques using simulation method, yielding favorable results. / Master of Science

En autorisant de nouveaux services centrés sur l'utilisateur (navigation indoor contextuelle, surveillance/inventaire de biens personnels, etc.), les nouvelles fonctions de radiolocalisation sont en passe de modifier en profondeur les usages liés à la mobilité. Dans ce contexte, la technologie radio ultra large bande impulsionnelle (IR-UWB), qui permet en théorie d'apprécier le temps de vol des impulsions transmises à l'échelle de la nanoseconde et donc, la distance séparant l'émetteur du récepteur avec une précision de l'ordre de quelques dizaines de centimètres seulement, a été régulièrement mise en avant ces dix dernières années. En dépit de ces bonnes dispositions, l'obstruction des liens radio par le(s) corps ou les obstacles (murs, mobilier...) donne toutefois lieu à des erreurs significatives sur les distances mesurées, dégradant d'autant les performances de positionnement, en particulier en environnements fermés (ex. indoor). Dans le cadre de cette thèse, on se proposait d'exploiter une architecture intégrée de récepteur IR-UWB, permettant d'estimer la réponse du canal multi-trajets dans son ensemble, afin d'améliorer la fonction de localisation. Une étude détaillée de ce canal radio mobile, tel que perçu par le récepteur, a d'abord été menée, débouchant sur une interprétation déterministe (c'est-à-dire géométrique) de l'évolution temporelle relative des composantes multi-trajets, ainsi qu'à une modélisation de leur interférence mutuelle. En s'appuyant sur l'étude précédente, des algorithmes de détection, d'association et de suivi des impulsions reçues (ex. batterie de filtres de Kalman à hypothèses multiples) ont alors été proposés. Ces différentes propositions tirent profit des spécificités de l'architecture du récepteur, en visant d'une part, à exploiter la cohérence spatio-temporelle des composantes multi-trajets résolues en réception, et d'autre part, à minimiser l'effet néfaste de leurs collisions au sein de canaux mobiles particulièrement denses (ex. via une estimation de canal multi-bandes). Les solutions apportées permettent en particulier, pour chaque lien radio en situation de non-visibilité, de corriger le temps d'arrivée des trajets directs manquants à partir de trajets secondaires suivis, tout en autorisant l'utilisation d'une structure de filtre classique pour la poursuite du mobile (c'est-à-dire, alimenté par plusieurs liens radio ainsi "corrigés" vis-à-vis de différentes balises fixes). Ces développements algorithmiques ont d'abord été validés par le biais de simulations (à partir d'un outil semi-déterministe, incluant un modèle de récepteur complet), avant d'être appliqués à un jeu de données réelles, issues de dispositifs radio IR-UWB commercialisés par la société BeSpoon. / By making possible unprecedented user-centric services (monitoring/smart inventory of personal goods, context-aware indoor navigation, etc.), new radiolocation capabilities are on the verge of modifying in depth mobility-based usages. In this context, the impulse radio - ultra wideband technology (IR-UWB), which theoretically enables to estimate the arrival time of transmitted pulses at the nanosecond scale and hence, the relative distance between a transmitter and a receiver within a few tens of centimeters, has been regularly put forward for the last past decade. In spite of these good intrinsic properties, the obstruction of radio links, either by the carrying body itself or by surrounding obstacles (walls, pieces of furniture. . . ), can result in significant errors on unitary range measurements, degrading the overall positioning performance accordingly, in particular in confined environments (e.g., indoor). In the frame of this PhD work, the main idea was to rely on an integrated IR-UWB receiver architecture, which has the capability to finely estimate the entire multipath profile, in order to improve the localization functionality. An in-depth study of the mobile multipath channel, as perceived by the previous receiver, has been conducted first, leading to the deterministic interpretation (i.e., from a geometric point of view) of the relative temporal evolution of multipath components, as well as to the modelling of their mutual interference. Based on these preliminary investigations, adapted multipath detection, association and tracking algorithms have been proposed (e.g., multi-hypothesis Kalman filters in parallel). All these proposals benefit from the receiver specificities, aiming at capturing the space-time correlation of multipath components under mobility, while minimizing harmful interference effects in dense channels (e.g., by means of combined multi-band channel estimations). In particular, for each non-line-of-sight link independently, the previous solutions allow to correct the estimated arrival time of the missing direct path out of the tracked secondary paths, while enabling the use of a conventional structure for the mobile tracking filter (i.e., fed by several "corrected" links with respect to distinct base stations). These algorithmic developments were first validated by means of simulations (using a semideterminist tool including a complete model of the receiver), before being applied to a measurement data set issued by IR-UWB devices commercialized by the BeSpoon company.

Les applications des techniques radar au milieu urbain constituent un domaine émergent. Une des difficultés principales est liée à la complexité du milieu de propagation induit par les bâtiments présents dans la scène. En effet, la présence de ces bâtiments génère d’une part des zones d’ombre à l’intérieur desquelles une cible n’est pas en visibilité directe, et d’autre part de nombreux multi-trajets produits par les possibles réflexions et diffractions sur les surfaces environnantes. Ces multi-trajets sont souvent vus comme une gêne, limitant les capacités de détection en radar. Or ils peuvent aussi être exploités à l’avantage du radar afin de détecter et localiser des cibles situées dans les zones d’ombre (cible en NLOS). L’objectif de ce travail de thèse est donc la mise en place de méthodes de traitement du signal permettant la détection et la localisation d’une cible en NLOS en milieu urbain et l’application de ces techniques pour détecter et localiser une cible en NLOS à partir de signaux réels. Pour cela, nous avons proposé dans un premier temps deux solutions pour la détection et la localisation de la cible en exploitant les multi-trajets. Dans un deuxième temps, nous avons développé deux filtres particulaires pour pister une cible en milieu urbain en présence de multi-trajets. Ces algorithmes ont été appliqués aux données réelles issues d’une expérimentation et ont montré des résultats prometteurs : même avec une connaissance approximative de la géométrie de la scène, il a été possible de détecter, localiser et suivre une cible en exploitant uniquement l’information fournie par les retards des multi-trajets. / The applications of radar techniques to the urban environment constitute an emerging subject. One of the main difficulties is related to the complexity of the propagation environment induced by the buildings present in the scene. Indeed, the presence of these buildings generates on the one hand shadow areas within which a target is not in line of sight, and on the other hand, many multipaths produced by reflections and diffractions on the surrounding surfaces. Classically, these multipaths are often seen as an inconvenience, limiting radar detection capabilities. However, these multipaths can also be exploited to the advantage of the radar to detect and locate targets located in the shadow areas (target in NLOS). The objective of this thesis work is therefore to develop signal processing methods allowing the detection and localization of a target located in shadow areas in urban environment and to apply these techniques for detecting and locating a target in NLOS from realistic or even real signals. For this, we first proposed two solutions for detection and localization of a target by exploiting multipath information. In a second step, we developed two particle filters to track a target in urban environment in the presence of multipaths. These algorithms have been applied to real data and showed promising results: even with an approximate knowledge of the geometry of the scene, it has been possible to detect, locate and track a target by exploiting only the information on multipath delays.