By using existing radio network infrastructure, a user can be positioned even where GPS and other positioning technologies lack coverage. The LTE Positioning Protocol (LPP) supports user Reference Signal Time Difference (RSTD) reports based on the Time of Arrival (TOA) for a Positioning Reference Signal (PRS). In the current reporting format, only one RSTD for each base station is considered, but for indoor environments this is easily biased due to fading and multipath issues, resulting in a Non-Line of Sight (NLOS) bias. With a rich User Equipment (UE) feedback that can represent the multipath channel for each Base Station (BS), the positioning accuracy can be increased. This thesis develops and evaluates a UE reporting format representing multiple TDOA candidates, and a probabilistic positioning algorithm, in terms of positioning accuracy and amount of data reported. By modeling time measurements as Gaussian Mixture (GM), the time information can be compressed with arbitrary resolution and used in a Maximum-Likelihood (ML) estimation to find the position. Results were obtained through simulation in a radio network simulator and post-processing of simulation data in Matlab. The results suggest that several TOA candidates improve the positioning accuracy, but that the largest improvement comes from a noise based threshold by increasing LOS detectability reducing the NLOS bias, while suppressing noise. The results also suggest that the accuracy for the method can be further improved by combining multiple time measurement occasions.

Ultra Wideband (UWB) signal can offer an accurate location service in wireless sensor networks because its high range resolution. Target tracking by multiple sensors can provide better performance, but the centralized algorithms are not suitable for wireless sensor networks. In additional, the non line of sight (NLOS) propagation error leads to severe degradation of the accuracy in location systems. In this thesis, NLOS identification and mitigation technique utilizing modified biased Kalman filter (KF) is proposed to reduce the NLOS time of arrival (TOA) errors in UWB environments. We combine the modified biased Kalman filter with sliding window to identify and mitigate different degree of NLOS errors immediately. In order to deal with the influence of inaccurate NLOS angle of arrival (AOA) measurements, we also had a discussion on AOA selection and fusion methods. In the distributed location structure, we used the extended Information filter (EIF) to process the formulated time difference of arrival (TDOA) and AOA measurements for the target positioning and tracking. Instead of using extended Kalman filter, extended Information filter can assimilate selected AOA easily without dynamic dimensions. The sensors are divided into different groups for distributed TDOA/AOA location to reduce computation and then each group can assimilate information from other groups easily to maintain precise location. The simulation results show that the proposed architecture can mitigate NLOS errors effectively and improve the accuracy of target positioning and tracking from distributed location and data fusion in wireless sensor networks.

One of the major problems encountered in wireless location is the effect caused by non-line of sight (NLOS) propagation. When the direct path from the mobile station (MS) to base stations (BSs) is blocked by obstacles or buildings, the signal arrival times will delay. That will make the signal measurements include an error due to the excess path propagation. If we use the NLOS signal measurements for localization, that will make the system localization performance reduce greatly. In the thesis, a time-of-arrival (TOA) based location system with NLOS mitigation algorithm is proposed. The proposed method uses least squares-support vector machine (LS-SVM) with optimal parameters selection by particle swarm optimization (PSO) for establishing regression model, which is used in the estimation of propagation distances and reduction of the NLOS propagation errors. By using a weighted objective function, the estimation results of the distances are combined with suitable weight factors, which are derived from the differences between the estimated measurements and the measured measurements. By applying the optimality of the weighted objection function, the method is capable of mitigating the NLOS effects and reducing the propagation range errors. Computer simulation results in ultra-wideband (UWB) environments show that the proposed NLOS mitigation algorithm can reduce the mean and variance of the NLOS measurements efficiently. The proposed method outperforms other methods in improving localization accuracy under different NLOS conditions.

High accuracy localization of objects is a crucial function for many modern applications, such as virtual and augmented reality, robotics, and self-driving cars, among others. This requires determining precise location of objects indoors, which is a challenging task. In recent years, Ultra-Wideband technology has seen increasing interest as a potential solution to this problem by the research community. This is mainly due to its innate capabilities of high update frequency and low power consumption which makes it a suitable technology for precise distance measurement and location determination. This study has aimed to answer what the state-of-the-art in the field of trilateration in Ultra-Wideband based indoor positioning systems utilizing other complementary technologies is. This was done by conducting a document survey using a Grounded theory approach for the analysis. To ensure validity and reliability of the study, the sample was collected through searching IEEE Xplore using different sets of keywords, and the potential samples was then checked using a data quality form. The analysis consisted of identifying categories and concepts in the sample. The analysis found that the Ultra-wideband based systems can achieve high positioning accuracy, but limitations such as non-line-of-sight disturbance must still be overcome for the technology to consistently achieve centimetre accuracy. These limitations are being mitigated using filtering, machine learning, and multi-sensory fusion. With these complementary technologies researchers can eliminate some of the limitations. The field does however seem to be in an exploratory stage where best practices for overcoming the current limitations are yet established.

Over the years, the Internet has become increasingly popular and people's dependence on it has increased dramatically. Whether it be to communicate to someone across the world, find blueprints, or check sports scores, the Internet has become a necessary resource for everyone. In emergency situations, this need increases further. After the terrorist attacks on the Pentagon, it took several days to restore communications. This is not an acceptable time frame when people's lives are at stake. Virginia Tech's Center for Wireless Telecommunication has developed a prototype of a rapidly deployable high bandwidth wireless communication system at 28 GHz (Local Multipoint Distribution Service frequency). This system provides a large bandwidth radio link to a disaster zone up to 5 km away and puts Ethernet speeds and 802.11 accesses to users within hours. Because of the possible variability in locations that the system can be deployed, it is necessary to find the most useable channel at the site as quickly as possible. In addition to 28GHz radio links, the system also has a built-in channel sounder that measures and captures the channel impulse response of the current channel. Until now, there has been limited research on the relationship between the channel impulse response and the usability of the channel quantified using bit error rate. This thesis examines several different channels captured by CWT's channel sounder and simulates the BER using Cadence's SPW with time-domain models of the channels. This thesis goes on further to show that BER greatly depends on the channel impulse response and the symbol rate. / Master of Science

Les avancées récentes dans le domaine de navigation par satellites (GNSS) ontconduit à une prolifération des applications de géolocalisation dans les milieux urbains. Pourde tels environnements, les applications GNSS souffrent d’une grande dégradation liée à laréception des signaux satellitaires en lignes indirectes (NLOS) et en multitrajets (MP). Cetravail de thèse propose une méthodologie originale pour l’utilisation constructive des signauxdégradés MP/NLOS, en appliquant des techniques avancées de traitement du signal ou àl’aide d’une assistance d’un simulateur 3D de propagation des signaux GNSS. D’abord, nousavons établi le niveau maximal réalisable sur la précision de positionnement par un systèmeGNSS "Stand-Alone" en présence de conditions MP/NLOS, en étudiant les bornes inférieuressur l’estimation en présence des signaux MP/NLOS. Pour mieux améliorer ce niveau deprécision, nous avons proposé de compenser les erreurs NLOS en utilisant un simulateur 3D dessignaux GNSS afin de prédire les biais MP/NLOS et de les intégrer comme des observationsdans l’estimation de la position, soit par correction des mesures dégradées ou par sélectiond’une position parmi une grille de positions candidates. L’application des approches proposéesdans un environnement urbain profond montre une bonne amélioration des performances depositionnement dans ces conditions. / Recent trends in Global Navigation Satellite System (GNSS) applications inurban environments have led to a proliferation of studies in this field that seek to mitigatethe adverse effect of non-line-of-sight (NLOS). For such harsh urban settings, this dissertationproposes an original methodology for constructive use of degraded MP/NLOS signals, insteadof their elimination, by applying advanced signal processing techniques or by using additionalinformation from a 3D GNSS simulator. First, we studied different signal processing frameworks,namely robust estimation and regularized estimation, to tackle this GNSS problemwithout using an external information. Then, we have established the maximum achievablelevel (lower bounds) of GNSS Stand-Alone positioning accuracy in presence of MP/NLOSconditions. To better enhance this accuracy level, we have proposed to compensate for theMP/NLOS errors using a 3D GNSS signal propagation simulator to predict the biases andintegrate them as observations in the estimation method. This could be either by correctingdegraded measurements or by scoring an array of candidate positions. Besides, new metricson the maximum acceptable errors on MP/NLOS errors predictions, using GNSS simulations,have been established. Experiment results using real GNSS data in a deep urban environmentshow that using these additional information provides good positioning performance enhancement,despite the intensive computational load of 3D GNSS simulation.

[pt] Este trabalho lida com o problema de radiolocalização de terminais em um ambiente de comunicações móveis celulares. Desenvolve-se novas alternativas para a estimação da posição, admitindo-se que as medidas de tempo de chegada (ToA) obtidas no enlace-rádio estão corrompidas por ruído aditivo e apresentam erro médio positivo durante períodos aleatórios, devido à ausência de linha de visada (NLOS) entre terminal e estações radio-bases. Em uma das alternativas desenvolve-se um estimador assintoticamente eficiente do erro de NLOS, sob o critério de mínimos quadrados ponderados (WLS). Para esta estimativa, admite- se o conhecimento a priori do espalhamento temporal do canal, e que o perfil de potência do sinal pode ser calculado por uma média temporal de medidas independentes em um receptor RAKE. O esquema de localização apresentado incorpora também um teste de hipóteses desenvolvido sob o critério de Neyman-Pearson, para detectar, a cada instante de tempo, a ocorrência de transições entre os estados LOS/NLOS do canal. Em outra contribuição do trabalho, as coordenadas do terminal são estimadas recursivamente utilizando-se algoritmos bayesianos, com a dimensão do espaço de estados aumentada para incluir o efeito do erro de NLOS sobre as medidas de ToA. Resultados de simulação obtidos sob diferentes cenários comprovam a eficiência dos esquemas de estimação aqui desenvolvidos, quando comparados à única solução de que se tem conhecimento na literatura. Apresenta-se ainda nesta tese uma análise para o problema de ambigüidade em métodos hiperbólicos de localização, cujo objetivo é identificar a região do plano em que este método fornece duas soluções fisicamente admissíveis. A área desta região é comparada com a área total de triangulação. / [en] This work addresses the radiolocation problem of a moving terminal in a cellular mobile communications environment. New alternatives are developed for position estimation, assuming that the Time of Arrival (ToA) measurements obtained from radio link are corrupted by additive noise and have positive mean error during random periods of time due to the non-line of sight (NLOS) propagation condition between the terminal and base stations. In one of the proposals, an asymptotically efficient WLS estimator of the NLOS error is developed under the Weighted Least Squares criterion. It is assumed that the channel temporal scattering model is known and the mean power delay profile can be evaluated by time averaging independent measurements from a RAKE receiver. The location estimation scheme also includes a hypothesis testing based on Neyman- Pearson approach to detect at each instant of time the LOS/NLOS states transitions. In another contribution, the terminal coordinates are recursively estimated using bayesian algorithms, with the state-space dimension augmented to include the NLOS error effect over ToA measurements. Simulation results obtained under different scenarios show the effectiveness of the estimation schemes developed here when compared to the only alternative known from the literature. An analysis concerning the ambiguity problem in hyperbolic location methods is also presented, aiming to determine the regions where this method gives two physically admissible solutions, and compare them to the total trilateration area.

First-generation, line-of-sight (LOS) fixed broadband wireless access techniques have been around for several years. However, services based on this technology have been limited in scope to service areas where transceivers can communicate with their base stations, unimpeded by trees, buildings and other obstructions. This limitation has serious consequences in that the system can deliver only 50% to 70% coverage within a given cell radius, thus affecting earned revenue. Next generation broadband fixed wireless access techniques are aimed at achieving a coverage area greater than 90%. To achieve this target, these techniques must be based on a point-to-multipoint (PMP) cellular architecture with low base station antennas, thus possessing the ability to operate in true non-line-of-sight (NLOS) conditions. A possible limiting factor for these systems is link degradation due to interference. This thesis presents a new model to estimate the levels of co-channel interference for such systems operating within the 3.5 GHz multichannel multipoint distribution service (MMDS) band. The model is site-specific in that it uses statistical building/roof height distribution parameters obtained from practically modeling several metropolitan cities in the U.S. using geographic information system (GIS) tools. This helps to obtain a realistic estimate and helps analyze the tradeoff between cell radius and modulation complexity. Together, these allow the system designer to decide on an optimal location for placement of customer premises equipment (CPE) within a given cell area. / Master of Science

The past two decades have seen a surge in the number of applications requiring precise positioning data. Modern cellular networks offer many services based on the user's location, such as emergency services (e.g., E911), and emerging wireless sensor networks are being used in applications spanning environmental monitoring, precision agriculture, warehouse and manufacturing logistics, and traffic monitoring, just to name a few. In these sensor networks in particular, obtaining precise positioning data of the sensors gives vital context to the measurements being reported. While the Global Positioning System (GPS) has traditionally been used to obtain this positioning data, the deployment locations of these cellular and sensor networks in GPS-constrained environments (e.g., cities, indoors, etc.), along with the need for reliable positioning, requires a localization scheme that does not rely solely on GPS. This has lead to localization being performed entirely by the network infrastructure itself, or by the network infrastructure aided, in part, by GPS. In the literature, benchmarking localization performance in these networks has traditionally been done in a deterministic manner. That is, for a fixed setup of anchors (nodes with known location) and a target (a node with unknown location) a commonly used benchmark for localization error, such as the Cramer-Rao lower bound (CRLB), can be calculated for a given localization strategy, e.g., time-of-arrival (TOA), angle-of-arrival (AOA), etc. While this CRLB calculation provides excellent insight into expected localization performance, its traditional treatment as a deterministic value for a specific setup is limited. Rather than trying to gain insight into a specific setup, network designers are more often interested in aggregate localization error statistics within the network as a whole. Questions such as: "What percentage of the time is localization error less than x meters in the network?" are commonplace. In order to answer these types of questions, network designers often turn to simulations; however, these come with many drawbacks, such as lengthy execution times and the inability to provide fundamental insights due to their inherent ``block box'' nature. Thus, this dissertation presents the first analytical solution with which to answer these questions. By leveraging tools from stochastic geometry, anchor positions and potential target positions can be modeled by Poisson point processes (PPPs). This allows for the CRLB of position error to be characterized over all setups of anchor positions and potential target positions realizable within the network. This leads to a distribution of the CRLB, which can completely characterize localization error experienced by a target within the network, and can consequently be used to answer questions regarding network-wide localization performance. The particular CRLB distribution derived in this dissertation is for fourth-generation (4G) and fifth-generation (5G) sub-6GHz networks employing a TOA localization strategy. Recognizing the tremendous potential that stochastic geometry has in gaining new insight into localization, this dissertation continues by further exploring the union of these two fields. First, the concept of localizability, which is the probability that a mobile is able to obtain an unambiguous position estimate, is explored in a 5G, millimeter wave (mm-wave) framework. In this framework, unambiguous single-anchor localization is possible with either a line-of-sight (LOS) path between the anchor and mobile or, if blocked, then via at least two NLOS paths. Thus, for a single anchor-mobile pair in a 5G, mm-wave network, this dissertation derives the mobile's localizability over all environmental realizations this anchor-mobile pair is likely to experience in the network. This is done by: (1) utilizing the Boolean model from stochastic geometry, which statistically characterizes the random positions, sizes, and orientations of reflectors (e.g., buildings) in the environment, (2) considering the availability of first-order (i.e., single-bounce) reflections as well as the LOS path, and (3) considering the possibility that reflectors can either facilitate or block reflections. In addition to the derivation of the mobile's localizability, this analysis also reveals that unambiguous localization, via reflected NLOS signals exclusively, is a relatively small contributor to the mobile's overall localizability. Lastly, using this first-order reflection framework developed under the Boolean model, this dissertation then statistically characterizes the NLOS bias present on range measurements. This NLOS bias is a common phenomenon that arises when trying to measure the distance between two nodes via the time delay of a transmitted signal. If the LOS path is blocked, then the extra distance that the signal must travel to the receiver, in excess of the LOS path, is termed the NLOS bias. Due to the random nature of the propagation environment, the NLOS bias is a random variable, and as such, its distribution is sought. As before, assuming NLOS propagation is due to first-order reflections, and that reflectors can either facilitate or block reflections, the distribution of the path length (i.e., absolute time delay) of the first-arriving multipath component (MPC) is derived. This result is then used to obtain the first NLOS bias distribution in the localization literature that is based on the absolute delay of the first-arriving MPC for outdoor time-of-flight (TOF) range measurements. This distribution is shown to match exceptionally well with commonly assumed gamma and exponential NLOS bias models in the literature, which were only attained previously through heuristic or indirect methods. Finally, the flexibility of this analytical framework is utilized by further deriving the angle-of-arrival (AOA) distribution of the first-arriving MPC at the mobile. This distribution gives novel insight into how environmental obstacles affect the AOA and also represents the first AOA distribution, of any kind, derived under the Boolean model. In summary, this dissertation uses the analytical tools offered by stochastic geometry to gain new insights into localization metrics by performing analyses over the entire ensemble of infrastructure or environmental realizations that a target is likely to experience in a network. / Doctor of Philosophy / The past two decades have seen a surge in the number of applications requiring precise positioning data. Modern cellular networks offer many services based on the user's location, such as emergency services (e.g., E911), and emerging wireless sensor networks are being used in applications spanning environmental monitoring, precision agriculture, warehouse and manufacturing logistics, and traffic monitoring, just to name a few. In these sensor networks in particular, obtaining precise positioning data of the sensors gives vital context to the measurements being reported. While the Global Positioning System (GPS) has traditionally been used to obtain this positioning data, the deployment locations of these cellular and sensor networks in GPS-constrained environments (e.g., cities, indoors, etc.), along with the need for reliable positioning, requires a localization scheme that does not rely solely on GPS. This has lead to localization being performed entirely by the network infrastructure itself, or by the network infrastructure aided, in part, by GPS. When speaking in terms of localization, the network infrastructure consists of what are called anchors, which are simply nodes (points) with a known location. These can be base stations, WiFi access points, or designated sensor nodes, depending on the network. In trying to determine the position of a target (i.e., a user, or a mobile), various measurements can be made between this target and the anchor nodes in close proximity. These measurements are typically distance (range) measurements or angle (bearing) measurements. Localization algorithms then process these measurements to obtain an estimate of the target position. The performance of a given localization algorithm (i.e., estimator) is typically evaluated by examining the distance, in meters, between the position estimates it produces vs. the actual (true) target position. This is called the positioning error of the estimator. There are various benchmarks that bound the best (lowest) error that these algorithms can hope to achieve; however, these benchmarks depend on the particular setup of anchors and the target. The benchmark of localization error considered in this dissertation is the Cramer-Rao lower bound (CRLB). To determine how this benchmark of localization error behaves over the entire network, all of the various setups of anchors and the target that would arise in the network must be considered. Thus, this dissertation uses a field of statistics called stochastic geometry} to model all of these random placements of anchors and the target, which represent all the setups that can be experienced in the network. Under this model, the probability distribution of this localization error benchmark across the entirety of the network is then derived. This distribution allows network designers to examine localization performance in the network as a whole, rather than just for a specific setup, and allows one to obtain answers to questions such as: "What percentage of the time is localization error less than x meters in the network?" Next, this dissertation examines a concept called localizability, which is the probability that a target can obtain a unique position estimate. Oftentimes localization algorithms can produce position estimates that congregate around different potential target positions, and thus, it is important to know when algorithms will produce estimates that congregate around a unique (single) potential target position; hence the importance of localizability. In fifth generation (5G), millimeter wave (mm-wave) networks, only one anchor is needed to produce a unique target position estimate if the line-of-sight (LOS) path between the anchor and the target is unimpeded. If the LOS path is impeded, then a unique target position can still be obtained if two or more non-line-of-sight (NLOS) paths are available. Thus, over all possible environmental realizations likely to be experienced in the network by this single anchor-mobile pair, this dissertation derives the mobile's localizability, or in this case, the probability the LOS path or at least two NLOS paths are available. This is done by utilizing another analytical tool from stochastic geometry known as the Boolean model, which statistically characterizes the random positions, sizes, and orientations of reflectors (e.g., buildings) in the environment. Under this model, considering the availability of first-order (i.e., single-bounce) reflections as well as the LOS path, and considering the possibility that reflectors can either facilitate or block reflections, the mobile's localizability is derived. This result reveals the roles that the LOS path and the NLOS paths play in obtaining a unique position estimate of the target. Using this first-order reflection framework developed under the Boolean model, this dissertation then statistically characterizes the NLOS bias present on range measurements. This NLOS bias is a common phenomenon that arises when trying to measure the distance between two nodes via the time-of-flight (TOF) of a transmitted signal. If the LOS path is blocked, then the extra distance that the signal must travel to the receiver, in excess of the LOS path, is termed the NLOS bias. As before, assuming NLOS propagation is due to first-order reflections and that reflectors can either facilitate or block reflections, the distribution of the path length (i.e., absolute time delay) of the first-arriving multipath component (MPC) (or first-arriving ``reflection path'') is derived. This result is then used to obtain the first NLOS bias distribution in the localization literature that is based on the absolute delay of the first-arriving MPC for outdoor TOF range measurements. This distribution is shown to match exceptionally well with commonly assumed NLOS bias distributions in the literature, which were only attained previously through heuristic or indirect methods. Finally, the flexibility of this analytical framework is utilized by further deriving angle-of-arrival (AOA) distribution of the first-arriving MPC at the mobile. This distribution yields the probability that, for a specific angle, the first-arriving reflection path arrives at the mobile at this angle. This distribution gives novel insight into how environmental obstacles affect the AOA and also represents the first AOA distribution, of any kind, derived under the Boolean model. In summary, this dissertation uses the analytical tools offered by stochastic geometry to gain new insights into localization metrics by performing analyses over all of the possible infrastructure or environmental realizations that a target is likely to experience in a network.

"A well-known challenge in estimating the distance of the antenna pair in time-of-arrival (ToA) based RF localization systems is the problem of obstruction of the direct path (DP) between transmitter and receiver. The absence of DP component in received channel profile creates undetected direct path (UDP) conditions. UDP condition, in turn, will cause occurrence of unexpected large ranging errors which pose serious challenge to precise indoor localization. Analysis of the behavior of the ranging error in such conditions is essential for the design of precise ToA-based indoor localization systems. This dissertation discusses two open problems in ToA-based indoor localization systems. The first contribution of this dissertation discusses the problem of modeling of dynamic behavior of ranging error. We propose a novel analytical framework for analysis of dynamic spatial variations of ranging error observed by a mobile user based on an application of Markov chain. The model relegates the behavior of ranging error into four main categories associated with four states of Markov process. Parameters of distributions of ranging error in each Markov state are extracted from empirical data collected from a measurement-calibrated ray tracing algorithm simulating a typical office environment. The analytical derivation of parameters of the Markov model employs the existing path-loss models for first detected path and total multipath received power in the same office environment. Results of simulated errors from the Markov model and actual errors from empirical data show close agreement. The second contribution of this dissertation discusses the problem of identification of UDP condition given an unknown channel profile. Existing of UDP condition in a channel profile poses serious degradation to ranging estimate process. Therefore, identification of occurrence of UDP condition is of our subsequent concern. After identification, the second step is to mitigate ranging errors in such conditions. In this dissertation we present two methodologies, based on binary hypothesis testing and an application of artificial neural network design, to identify UDP conditions and mitigate ranging error using statistics extracted from wideband frequency-domain indoor measurements conducted in typical office building. "