"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. "

Networks with hundreds of ad hoc nodes equipped with communication and position finding abilities are conceivable with recent advancements in technology. Methods are presented in this thesis to assess the communicative capabilities and node position estimation of mobile ad hoc networks. Specifically, we investigate techniques for providing communication and geolocation with specific characteristics in wireless ad hoc networks. The material presented in this thesis, communication and geolocation, may initially seem a collection of disconnected topics related only distantly under the banner of ad hoc networks. However, systems currently in development combining these techniques into single integrated systems. In this thesis first, we investigate the effect of multilayer interaction, including fading and path loss, on ad hoc routing protocol performance, and present a procedure for deploying an ad hoc network based on extensive simulations. Our first goal is to test the routing protocols with parameters that can be used to characterize the environment in which they might be deployed. Second, we analyze the location discovery problem in ad hoc networks and propose a fully distributed, infrastructure-free positioning algorithm that does not rely on the Global Positioning System (GPS). The algorithm uses the approximate distances between the nodes to build a relative coordinate system in which the node positions are computed in three-dimensions. However, in reconstructing three-dimensional positions from approximate distances, we need to consider error threshold, graph connectivity, and graph rigidity. We also statistically evaluate the location discovery procedure with respect to a number of parameters, such as error propagation and the relative positions of the nodes.

A geolocalização, os dispositivos móveis cada vez mais sofisticados e eficientes e a disponibilidade instantânea de mapas de todos os lugares do mundo, permitiram o desenvolvimento de novos aplicativos, os chamados serviços baseados em geolocalização; além disso, as redes sociais online têm se consagrado como ferramentas de relações entre pessoas. A combinação destas tecnologias levou ao surgimento de programas como o Foursquare, que alcançou a marca dos 30 milhões de usuários em 2013, quatro anos depois de seu lançamento, o que mostra a importância deste tipo de aplicativo; através do estudo destes aplicativos, observa-se a valorização da vizinhança como fator de afinidade social. Da análise dos elementos de wayfinding, depreende-se que a cidade incorpora marcos com valor emocional. Ao final, é proposto um sistema gráfico que torne visíveis estas referências emocionais. / Geolocation, mobile devices increasingly sophisticated and efficient and instantaneous availability of maps from all over the world, allowed the development of new applications, the so-called geolocation based services, in addition, online social networks have been established as tools for relation between people. The combination of these technologies has led to the emergence of programs like Foursquare, which reached 30 million users in 2013, four years after its release, which shows the importance of this type of application; through the research of those applications, there is the appreciation of the neighborhood as a factor of social affinity. From the analysis of the wayfinding is implicit that the city incorporates marks with emotional value. A graphical system is proposed to turn visible these emotional references.

In this thesis, the Recursive-Random Sample Consensus (R-RANSAC) algorithm is applied to a vision-based, cooperative target tracking system. Unlike previous applications, which focused on a single camera platform tracking targets in the image frame, this work uses multiple camera platforms to track targets in the inertial or world frame. The process of tracking targets in the inertial frame is commonly referred to as geolocation.In practical applications sensor biases cause the geolocated target estimates to be biased from truth. The method for cooperative estimation developed in this thesis first estimates the relative rotational and translational biases that exist between tracks from different vehicles. It then accounts for the biases and performs the track-to-track association, which determines if the tracks originate from the same target. The track-to-track association is based on a sliding window approach that accounts for the correlation between tracks sharing common process noise and the correlation in time between individual estimation errors, yielding a chi-squared distribution. Typically, accounting for the correlation in time requires the inversion of a Nnx x Nnx covariance matrix, where N is the length of the window and nx is the number of states. Note that this inversion must occur every time the track-to-track association is to be performed. However, it is shown that by making a steady-state assumption, the inverse has a simple closed-form solution, requiring the inversion of only two nx x nx matrices, and can be calculated offline. Distributed data fusion is performed on tracks where the hypothesis test is satisfied. The proposed method is demonstrated on data collected from an actual vision-based tracking system.A novel method is also developed to cooperatively estimate the location and size of occlusions. This capability is important for future target tracking research involving optimized path planning/gimbal pointing, where a geographical map is unavailable. The method is demonstrated in simulation.

We consider time-of-arrival based robust geolocation in harsh line-of-sight/non-line-of-sight environments. Herein, we assume the probability density function (PDF) of the measurement error to be completely unknown and develop an iterative algorithm for robust position estimation. The iterative algorithm alternates between a PDF estimation step, which approximates the exact measurement error PDF (albeit unknown) under the current parameter estimate via adaptive kernel density estimation, and a parameter estimation step, which resolves a position estimate from the approximate log-likelihood function via a quasi-Newton method. Unless the convergence condition is satisfied, the resolved position estimate is then used to refine the PDF estimation in the next iteration. We also present the best achievable geolocation accuracy in terms of the Cramér-Rao lower bound. Various simulations have been conducted in both real-world and simulated scenarios. When the number of received range measurements is large, the new proposed position estimator attains the performance of the maximum likelihood estimator (MLE). When the number of range measurements is small, it deviates from the MLE, but still outperforms several salient robust estimators in terms of geolocation accuracy, which comes at the cost of higher computational complexity.

Advances in wireless communication have enabled mobility of personal computing services equipped with sensing and computing capabilities. This has motivated the development of location-based services (LBS) that are implemented on top of existing communication infrastructures to cater to changing user contexts. To enable and support the delivery of LBS, accurate, reliable, and realtime user location information is needed. This thesis introduces a cognitive dynamic system for tracking the position of mobile users using received signal strength (RSS) in Wireless Local Area Networks (WLAN). The main challenge in WLAN positioning is the unpredictable nature of the RSS-position relationship. Existing system rely on a set of training samples collected at a set of anchor points with known positions in the environment to characterize this relationship. The first contribution of this thesis is the use of nonparametric kernel density estimation for minimum mean square error positioning using the RSS training data. This formulation enables the rigorous study of state-space filtering in the context of WLAN positioning. The outcome is the Nonparametric Information (NI) filter, a novel recursive position estimator that incorporates both RSS measurements and a dynamic model of pedestrian motion during estimation. In contrast to traditional Kalman filtering approaches, the NI filter does not require the explicit knowledge of RSS-position relationship and is therefore well-suited for the WLAN positioning problem. The use of the dynamic motion model by the NI filter leads to the design of a cognitive dynamic tracking system. This design harnesses the benefits of feedback and position predictions from the filter to guide the selection of anchor points and radio sensors used during estimation. Experimental results using real measurement from an office environment demonstrate the effectiveness of proactive determination of sensing and estimation parameters in mitigating difficulties that arise due to the unpredictable nature of the indoor radio environment. In particular, the results indicate that the proposed cognitive design achieves an improvement of 3.19m (56\%) in positioning error relative to memoryless positioning alone.

Advances in wireless communication have enabled mobility of personal computing services equipped with sensing and computing capabilities. This has motivated the development of location-based services (LBS) that are implemented on top of existing communication infrastructures to cater to changing user contexts. To enable and support the delivery of LBS, accurate, reliable, and realtime user location information is needed. This thesis introduces a cognitive dynamic system for tracking the position of mobile users using received signal strength (RSS) in Wireless Local Area Networks (WLAN). The main challenge in WLAN positioning is the unpredictable nature of the RSS-position relationship. Existing system rely on a set of training samples collected at a set of anchor points with known positions in the environment to characterize this relationship. The first contribution of this thesis is the use of nonparametric kernel density estimation for minimum mean square error positioning using the RSS training data. This formulation enables the rigorous study of state-space filtering in the context of WLAN positioning. The outcome is the Nonparametric Information (NI) filter, a novel recursive position estimator that incorporates both RSS measurements and a dynamic model of pedestrian motion during estimation. In contrast to traditional Kalman filtering approaches, the NI filter does not require the explicit knowledge of RSS-position relationship and is therefore well-suited for the WLAN positioning problem. The use of the dynamic motion model by the NI filter leads to the design of a cognitive dynamic tracking system. This design harnesses the benefits of feedback and position predictions from the filter to guide the selection of anchor points and radio sensors used during estimation. Experimental results using real measurement from an office environment demonstrate the effectiveness of proactive determination of sensing and estimation parameters in mitigating difficulties that arise due to the unpredictable nature of the indoor radio environment. In particular, the results indicate that the proposed cognitive design achieves an improvement of 3.19m (56\%) in positioning error relative to memoryless positioning alone.

Localisation, which is a mechanism for discovering the spatial relationship between objects, is an area that has received considerable research and development in recent times. A common name given to localisation operations based on the absolute reference frame of Earth is Geolocation. One important example of geolocation research is E-911, where wireless carriers in the United States must provide the location of 911 callers. The operation of E-911 can be based on either a network configuration, or the Global Positioning System (GPS). With the importance of localisation being acknowledged, a review concerning the vulnerability of the Global Navigation Satellite System (GNSS) is provided as background and motivation for this research. With the current vulnerability of GNSS, this dissertation presents the results of a research program undertaken with the objective of developing an electromagnetic localisation technique that can determine the relative position of GPS Radio Frequency Interference (RFI) sources. Intended for operation in a hostile environment, blind and passive localisation methodologies must be incorporated into the developed model. In performing localisation research, a background of current techniques is provided in addition to a review of current electromagnetic propagation models. From the review of propagation models, the Parabolic Equation Model (PEM) was chosen for investigation concerning localisation. The selection of PEM is due to model properties that are required for blind/passive localisation. The localisation system developed in this research program is based on the integration of inverse diffraction propagation (IDP) within the parabolic equation model. The title chosen for the localisation method is Inverse Diffraction Parabolic Equation Localisation System (IDPELS). This thesis presents the simulation and field trial results of IDPELS. Under simulation, the terrain or obstacle profiles were not based on any geodetic datum. Any estimate provided by IDPELS under simulation is therefore a "Localisation" solution. In the field trials however, IDPELS operation is referred to as "Geolocation" as geodetic datum's where used to determine the receiver's position. Under simulation analysis, IDPELS operation was considered to provide good promise as it could simultaneously perform localisation on multiple transmission sources. In each investigated simulation scenario, a display of signals amplitude (dB units) is displayed over the entire region. By determining the field convergence regions, a localisation estimate of IDPELS is provided. By defining the convergence regions as areas having the greatest signal amplitude values (i.e. ≥ 99%), elliptical areas as low as 3.2m² were considered to indicate an excellent localisation capability. With the theoretical validity of IDPELS operation in electromagnetics having been established under simulation, further investigation into the practical feasibility of the IDPELS was performed. The field trials positioned a continuous-wave (CW) transmission source at a known location. By measuring signal phasors along a straight section of road, the geodetic spatial-phase profile was used as the input signal for IDPELS. Road sections used were cross-wise to the transmitter's boresight. Many data sets were recorded, each being made over a sixty second time period. Different regions and ranges where used to continuously measure the spatial-phase profile of the signal with fixed antennas in a moving vehicle. Such a measurement process introduced an analogy with Synthetic Aperture Radar (SAR) processes. In quantitating the accuracy of the IDPELS geolocation estimate in field trials, the linear error of range and cross-range components was analysed. A free-space PEM model was chosen for development of IDPELS and hence, data sets demonstrating properties of a free-space environment were able to be considered suitable for testing of the geolocation method. Data sets demonstrating free-space propagation characteristics were measured at the base of the Mt Lofty ranges in South Australia, where the range and cross-range error are respectively 3.14m, and 0.15m. Such low error values clearly demonstrate the practical feasibility of IDPELS geolocation. With the practical feasibility of IDPELS having been established in this research program, a novel contribution to electromagnetic geolocation methodologies is provided. An important characteristic of any geolocation technique concerns its robustness to operate in a wide variety of possible environments. With continued development of IDPELS, the robustness of this passive/blind geolocation technique can be enhanced. Further assistance with geolocation of multiple transmission sources is also indicated to be available by IDPELS, as shown in the simulation analysis.

The goal of this thesis is to characterize a specific application of mobile marketing, particularly the application of location based mobile marketing by the company H&M in the Czech Republic. The company is one of the largest global clothing retailers. The thesis is divided into a theoretical and practical part. The first one was completed by researching specialized literature and online resources and was crucial for understanding and completing the actual analysis of the specific mobile marketing application in the practical part of the thesis. The second part of the thesis contains the description of the company, its history, company values and concept. Furthermore, the work includes the analysis of the marketing mix with emphasis on the communication mix of the company. In the actual analysis of the specific application of mobile marketing in the company H&M, I described the geolocation SMS campaign and tried to submit improvement proposals for future use of this type of mobile marketing campaign and also suggested other possibilities in using location based mobile marketing in the company.

A geolocalização, os dispositivos móveis cada vez mais sofisticados e eficientes e a disponibilidade instantânea de mapas de todos os lugares do mundo, permitiram o desenvolvimento de novos aplicativos, os chamados serviços baseados em geolocalização; além disso, as redes sociais online têm se consagrado como ferramentas de relações entre pessoas. A combinação destas tecnologias levou ao surgimento de programas como o Foursquare, que alcançou a marca dos 30 milhões de usuários em 2013, quatro anos depois de seu lançamento, o que mostra a importância deste tipo de aplicativo; através do estudo destes aplicativos, observa-se a valorização da vizinhança como fator de afinidade social. Da análise dos elementos de wayfinding, depreende-se que a cidade incorpora marcos com valor emocional. Ao final, é proposto um sistema gráfico que torne visíveis estas referências emocionais. / Geolocation, mobile devices increasingly sophisticated and efficient and instantaneous availability of maps from all over the world, allowed the development of new applications, the so-called geolocation based services, in addition, online social networks have been established as tools for relation between people. The combination of these technologies has led to the emergence of programs like Foursquare, which reached 30 million users in 2013, four years after its release, which shows the importance of this type of application; through the research of those applications, there is the appreciation of the neighborhood as a factor of social affinity. From the analysis of the wayfinding is implicit that the city incorporates marks with emotional value. A graphical system is proposed to turn visible these emotional references.