The objective of the thesis is concerned on the problem of detecting superpositional signals in a wireless channel. In many wireless systems, an observed signal is commonly represented as a linear combination of the transmitted signal with the interfering signals dispersed in space and time. These systems are generally known as the interference-limited systems. The mathematical model of these systems is generally referred as a superpositional model. A distinguished characteristic of signal transmission in a time-varying wireless channel is that the channel process is not known a priori. Reliable signal reception inherently requires exploiting the structure of the interfering signals under channel uncertainty. Our goal is to design computational efficient receivers for various interference-limited systems by using advanced statistical signal processing techniques. The thesis consists of four main parts. Firstly, we have proposed a novel Multi-Input Multi-Output (MIMO) signal detector, known as the neighbourhood exploring detector (NED). According to the maximum likelihood principle, the space time MIMO detection problem is equivalent to a NP-hard combinatorial optimization problem. The proposed detector is a sub-optimal maximum likelihood detector which eliminates exhaustive multidimensional searches. Secondly, we address the problem of signal synchronization for Global Positioning System (GPS) in a multipath environment. The problem of multipath mitigation constitutes a joint estimation of the unknown amplitudes, phases and time delays of the linearly combined signals. The complexity of the nonlinear joint estimation problem increases exponentially with the number of signals. We have proposed two robust GPS code acquisition systems with low complexities. Thirdly, we deal with the problem of multipath mitigation in the spatial domain. A GPS receiver integrated with the Inertial Navigation System (INS) and a multiple antenna array is considered. We have designed a software based GPS receiver which effectively estimates the directions of arrival and the time of arrival of the linearly combined signals. Finally, the problem of communications with unknown channel state information is investigated. Conventionally, the information theoretical communication problem and the channel estimation problem are decoupled. However the training sequence, which facilitates the estimation of the channel, reduces the throughput of the channel. We have analytically derived the optimal length of the training sequence which maximizes the mutual information in a block fading channel.

This research concerns the development of a smart sensory system for tracking a hand-held moving device to millimeter accuracy, for slow or nearly static applications over extended periods of time. Since different operators in different applications may use the system, the proposed design should provide the accurate position, orientation, and velocity of the object without relying on the knowledge of its operation and environment, and based purely on the motion that the object experiences. This thesis proposes the design of the integration a low-cost Local Positioning System (LPS) and a low-cost StrapDown Inertial Navigation System (SDINS) with the association of the modified EKF to determine 3D position and 3D orientation of a hand-held tool within a required accuracy. A hybrid LPS/SDINS combines and complements the best features of two different navigation systems, providing a unique solution to track and localize a moving object more precisely. SDINS provides continuous estimates of all components of a motion, but SDINS loses its accuracy over time because of inertial sensors drift and inherent noise. LPS has the advantage that it can possibly get absolute position and velocity independent of operation time; however, it is not highly robust, is computationally quite expensive, and exhibits low measurement rate. This research consists of three major parts: developing a multi-camera vision system as a reliable and cost-effective LPS, developing a SDINS for a hand-held tool, and developing a Kalman filter for sensor fusion. Developing the multi-camera vision system includes mounting the cameras around the workspace, calibrating the cameras, capturing images, applying image processing algorithms and features extraction for every single frame from each camera, and estimating the 3D position from 2D images. In this research, the specific configuration for setting up the multi-camera vision system is proposed to reduce the loss of line of sight as much as possible. The number of cameras, the position of the cameras with respect to each other, and the position and the orientation of the cameras with respect to the center of the world coordinate system are the crucial characteristics in this configuration. The proposed multi-camera vision system is implemented by employing four CCD cameras which are fixed in the navigation frame and their lenses placed on semicircle. All cameras are connected to a PC through the frame grabber, which includes four parallel video channels and is able to capture images from four cameras simultaneously. As a result of this arrangement, a wide circular field of view is initiated with less loss of line-of-sight. However, the calibration is more difficult than a monocular or stereo vision system. The calibration of the multi-camera vision system includes the precise camera modeling, single camera calibration for each camera, stereo camera calibration for each two neighboring cameras, defining a unique world coordinate system, and finding the transformation from each camera frame to the world coordinate system. Aside from the calibration procedure, digital image processing is required to be applied into the images captured by all four cameras in order to localize the tool tip. In this research, the digital image processing includes image enhancement, edge detection, boundary detection, and morphologic operations. After detecting the tool tip in each image captured by each camera, triangulation procedure and optimization algorithm are applied in order to find its 3D position with respect to the known navigation frame. In the SDINS, inertial sensors are mounted rigidly and directly to the body of the tracking object and the inertial measurements are transformed computationally to the known navigation frame. Usually, three gyros and three accelerometers, or a three-axis gyro and a three-axis accelerometer are used for implementing SDINS. The inertial sensors are typically integrated in an inertial measurement unit (IMU). IMUs commonly suffer from bias drift, scale-factor error owing to non-linearity and temperature changes, and misalignment as a result of minor manufacturing defects. Since all these errors lead to SDINS drift in position and orientation, a precise calibration procedure is required to compensate for these errors. The precision of the SDINS depends not only on the accuracy of calibration parameters but also on the common motion-dependent errors. The common motion-dependent errors refer to the errors caused by vibration, coning motion, sculling, and rotational motion. Since inertial sensors provide the full range of heading changes, turn rates, and applied forces that the object is experiencing along its movement, accurate 3D kinematics equations are developed to compensate for the common motion-dependent errors. Therefore, finding the complete knowledge of the motion and orientation of the tool tip requires significant computational complexity and challenges relating to resolution of specific forces, attitude computation, gravity compensation, and corrections for common motion-dependent errors. The Kalman filter technique is a powerful method for improving the output estimation and reducing the effect of the sensor drift. In this research, the modified EKF is proposed to reduce the error of position estimation. The proposed multi-camera vision system data with cooperation of the modified EKF assists the SDINS to deal with the drift problem. This configuration guarantees the real-time position and orientation tracking of the instrument. As a result of the proposed Kalman filter, the effect of the gravitational force in the state-space model will be removed and the error which results from inaccurate gravitational force is eliminated. In addition, the resulting position is smooth and ripple-free. The experimental results of the hybrid vision/SDINS design show that the position error of the tool tip in all directions is about one millimeter RMS. If the sampling rate of the vision system decreases from 20 fps to 5 fps, the errors are still acceptable for many applications.

This research concerns the development of a smart sensory system for tracking a hand-held moving device to millimeter accuracy, for slow or nearly static applications over extended periods of time. Since different operators in different applications may use the system, the proposed design should provide the accurate position, orientation, and velocity of the object without relying on the knowledge of its operation and environment, and based purely on the motion that the object experiences. This thesis proposes the design of the integration a low-cost Local Positioning System (LPS) and a low-cost StrapDown Inertial Navigation System (SDINS) with the association of the modified EKF to determine 3D position and 3D orientation of a hand-held tool within a required accuracy. A hybrid LPS/SDINS combines and complements the best features of two different navigation systems, providing a unique solution to track and localize a moving object more precisely. SDINS provides continuous estimates of all components of a motion, but SDINS loses its accuracy over time because of inertial sensors drift and inherent noise. LPS has the advantage that it can possibly get absolute position and velocity independent of operation time; however, it is not highly robust, is computationally quite expensive, and exhibits low measurement rate. This research consists of three major parts: developing a multi-camera vision system as a reliable and cost-effective LPS, developing a SDINS for a hand-held tool, and developing a Kalman filter for sensor fusion. Developing the multi-camera vision system includes mounting the cameras around the workspace, calibrating the cameras, capturing images, applying image processing algorithms and features extraction for every single frame from each camera, and estimating the 3D position from 2D images. In this research, the specific configuration for setting up the multi-camera vision system is proposed to reduce the loss of line of sight as much as possible. The number of cameras, the position of the cameras with respect to each other, and the position and the orientation of the cameras with respect to the center of the world coordinate system are the crucial characteristics in this configuration. The proposed multi-camera vision system is implemented by employing four CCD cameras which are fixed in the navigation frame and their lenses placed on semicircle. All cameras are connected to a PC through the frame grabber, which includes four parallel video channels and is able to capture images from four cameras simultaneously. As a result of this arrangement, a wide circular field of view is initiated with less loss of line-of-sight. However, the calibration is more difficult than a monocular or stereo vision system. The calibration of the multi-camera vision system includes the precise camera modeling, single camera calibration for each camera, stereo camera calibration for each two neighboring cameras, defining a unique world coordinate system, and finding the transformation from each camera frame to the world coordinate system. Aside from the calibration procedure, digital image processing is required to be applied into the images captured by all four cameras in order to localize the tool tip. In this research, the digital image processing includes image enhancement, edge detection, boundary detection, and morphologic operations. After detecting the tool tip in each image captured by each camera, triangulation procedure and optimization algorithm are applied in order to find its 3D position with respect to the known navigation frame. In the SDINS, inertial sensors are mounted rigidly and directly to the body of the tracking object and the inertial measurements are transformed computationally to the known navigation frame. Usually, three gyros and three accelerometers, or a three-axis gyro and a three-axis accelerometer are used for implementing SDINS. The inertial sensors are typically integrated in an inertial measurement unit (IMU). IMUs commonly suffer from bias drift, scale-factor error owing to non-linearity and temperature changes, and misalignment as a result of minor manufacturing defects. Since all these errors lead to SDINS drift in position and orientation, a precise calibration procedure is required to compensate for these errors. The precision of the SDINS depends not only on the accuracy of calibration parameters but also on the common motion-dependent errors. The common motion-dependent errors refer to the errors caused by vibration, coning motion, sculling, and rotational motion. Since inertial sensors provide the full range of heading changes, turn rates, and applied forces that the object is experiencing along its movement, accurate 3D kinematics equations are developed to compensate for the common motion-dependent errors. Therefore, finding the complete knowledge of the motion and orientation of the tool tip requires significant computational complexity and challenges relating to resolution of specific forces, attitude computation, gravity compensation, and corrections for common motion-dependent errors. The Kalman filter technique is a powerful method for improving the output estimation and reducing the effect of the sensor drift. In this research, the modified EKF is proposed to reduce the error of position estimation. The proposed multi-camera vision system data with cooperation of the modified EKF assists the SDINS to deal with the drift problem. This configuration guarantees the real-time position and orientation tracking of the instrument. As a result of the proposed Kalman filter, the effect of the gravitational force in the state-space model will be removed and the error which results from inaccurate gravitational force is eliminated. In addition, the resulting position is smooth and ripple-free. The experimental results of the hybrid vision/SDINS design show that the position error of the tool tip in all directions is about one millimeter RMS. If the sampling rate of the vision system decreases from 20 fps to 5 fps, the errors are still acceptable for many applications.

Disasters (e.g. earthquakes) and emergencies (e.g. fire) threaten the safety of occupants in the buildings and cause injuries and mortalities. These harmful effects are even more dangerous when secondary hazards (e.g. post-earthquake fires) emerge and it is commonly observed that the disasters/emergencies trigger secondary hazards.An effective indoor emergency guidance and navigation approach for occupants and first responders can decrease the number of injuries and mortalities during building emergencies by improving the evacuation process and response operations. For this reason, this research will propose a model-based guidance and vulnerability assessment approach for facilities that are under the threat of multi-hazard emergencies. The approach can be used to guide occupants from the facility affected by disasters/emergencies to safer zones and to direct the first responders by supplying them necessary building related information such as identified vulnerable locations in the indoor environments. An integrated utilization of Building Information Modeling tools, sensors, shortest path algorithms, and vulnerability assessment algorithms is proposed for the system in this research. The research steps of this thesis include (1) determination of requirements of an indoor navigation during emergency response and disaster management,(2) review, comparison, and evaluation of shortest path algorithms from an emergency response and disaster management point of view, (3) proposing a vulnerability assessment approach, and (4) proposing a real-time indoor emergency guidance and navigation system framework for buildings under the threat of multi-hazard emergencies. The findings of the research can be used in future studies on emergency response and disaster management domains.

Baigiamajame magistro darbe nagrinėjamos palydovinės tikslaus tūpimo pagal prietaisus sistemos taikymo Lietuvos aviacijoje galimybės. Aptartos palydovinės navigacijos sistemos tikslumą įtakojančios paklaidos, jų šaltiniai. Nagrinėjama GBAS sistemos struktūra, veikimo principas, paklaidų eliminavimo būdai. Išnagrinėtos šios sistemos panaudojimo galimybės (infrastruktūros ir ekonominiu požiūriais) tarptautiniuose Vilniaus, Kauno, Palangos ir Šiaulių oro uostuose. Atlikti bandymai Vilniaus tarptautiniame oro uoste, įvertinant potencialias GPS paklaidas bei įvertinant GBAS sistemos tikslumo ir tinkamumo galimybes. Išnagrinėjus teorinius ir praktinius tyrimo rezultatus, pateikiamos baigiamojo darbo išvados ir siūlymai. Darbą sudaro 9 dalys: įvadas, analitinė dalis, palydovinės radijo navigacinės sistemos antžeminė patikslinimo sis-tema, diferencinės pataisos, tikslaus artėjimo tūpti GBAS sistema, tiriamoji dalis, rezultatų apibendrinimas, literatūros sąrašas, priedai. Darbo apimtis – 63 p. teksto be priedų, 40 iliustr., 11 lent., 34 bibliografiniai šaltiniai. Atskirai pridedami darbo priedai. / This master’s thesis explores possibilities of satellite precision instrument landing application for Lithuanian avia-tion. At the first part of the thesis satellite navigation errors affecting its accuracy and their sources were examined. GBAS system stricture, its operation and error elimination methods were reviewed. Further explored were possibilities of imple-menting the analyzed system (infrastructure and economic terms) in international Vilnius, Kaunas, Palanga and Šiauliai airports. Test were carried out in Vilnius international airport aiming for assessment of potential GPS signal errors and estimation of GBAS systems accuracy and fitness opportunities, followed by theoretical and practical examination of the analysis data collected. Based on the examination results final thesis conclusions and recommendations are formulated and provided at the ending of this thesis. Structure: introduction, analytical part, ground based augmentation system, diferential corrections, precision lan-ding GBAS system, exploratory part, conclusions and suggestions, references. Thesis consist of: 63 p. text without appendixes, 40 pictures, 11 tables, 34 bibliographical entries. Appendixes included.

In this thesis, robust set-valued estimation is studied and its application to in-flight alignment of strapdown inertial navigation systems (SINS) with large heading uncertainty is performed. It is known that the performance of the Kalman filter is vulnerable to modeling errors. One of the estimation methods, which are robust against modeling errors, is robust set-valued estimation. In this approach, the filter calculates the set of all possible states, which are consistent with uncertainty inputs satisfying an integral quadratic constraint (IQC) for given measured system outputs. In this thesis, robust set-valued filter with deterministic input is derived. In-flight alignment of SINS with Kalman filtering using external measurements is a widely used technique to eliminate the initial errors. However, if the initial errors are large then the performance of standard Kalman filtering technique is degraded due to modeling error caused by linearization process. To solve this problem, a novel linear norm-bounded uncertain error model is proposed where the remaining second orders terms due to linearization process are considered as norm-bounded uncertainty regarding only the heading error is large. Using the uncertain error model, the robust set-valued filter is applied to in-flight alignment problem. The comparison of the Kalman filter and the robust filter is done on a simulated trajectory and a real-time data. The simulation results show that the modeling errors can be compensated to some extent in Kalman filter by increasing the process noise covariance matrix. However, for very large initial heading errors, the proposed method outperforms the Kalman filter.

Despite significant progress in GPS/INS integration-based direct geo-referencing (DGR) technology over the past decade, its performance still needs to be improved in terms of accuracy and tolerance to GPS outages. This is mainly due to the limited geometric strength of the GPS satellite constellation, the quality of INS and the system integration technology. This research is focused on pseudolite (PL) augmentation to enhance the geometric strength of the GPS satellite constellation, and the Neural Network (NN) aided Kalman filter (KF) system integration algorithm to improve the geo-referencing system's performance during GPS outages. The main research contributions are summarized as below: a) Systematic errors introduced by pseudolites have been investigated. Theoretical and numerical analyses reveal that errors of troposphere delay modelling, differential nonlinearity and pseudolite location are sensitive to pseudolite receiver geometry. Their effect on final positioning solutions can be minimised by selecting optimal pseudolite and receiver locations, which is referred to as geometry design. Optimal geometry design for pseudolite augmented systems has been proposed based on simulation results in airborne surveying scenarios. b) Nonlinear geometry bias, or nonlinearity, exists in single difference processes when the unit vectors from the reference and user receivers to a satellite or pseudolite are non-parallel. Similar to long baseline differential GPS (DGPS), nonlinearity is a serious issue in pseudolite augmentation. A Projected Single Difference (PSD) method has been introduced to eliminate nonlinear geometry bias. An optimized expression has been derived to calculate the direction of project vectors, and the advantages of applying PSD in pseudolite augmented airborne DGPS have been demonstrated. c) A new method for pseudolite tropospheric delay modelling has been proposed, which is based on single-differenced GPS tropospheric delay models. The performance of different models has been investigated through simulations and field testing. The advantages and limitations of each method have been analysed. It is determined that the Bouska model performs relatively well in all ranges and elevations if the meteorological parameters in the models can be accurately collected. d) An adaptive pseudolite tropospheric delay modelling method has been developed to reduce modelling error by estimating meteorological parameters in real-time, using GPS and pseudolite measurements. Test results show that pseudolite tropospheric delay modelling errors can be effectively mitigated by the proposed method. e) A novel geo-referencing system based on GPS/PL/INS integration has been developed as an alternative to existing GPS/INS systems. With the inclusion of pseudolite signals to enhance availability and geometry strength of GPS signals, the continuity and precision of the GPS/INS system can be significantly improved. Flight trials have been conducted to evaluate the system performance for airborne mapping. The results show that the accuracy and reliability of the geo-referenced solution can be improved with the deployment of one or more pseudolites. f) Two KF and NN hybrid methods have been proposed to improve geo-referenced results during GPS outages. As the KF prediction diverges without measurement update, the performance of a GPS/INS integrated system degrades rapidly during GPS outages. Neural networks can overcome this limitation of KF. The first method uses NN to map vehicle manoeuvres with KF measurement in a loosely coupled GPS/INS system. In the second method, an NN is trained to map INS measurements with selected KF error states in a tightly coupled GPS/INS system when GPS signals are available. These training results can be used to modify KF time updates. Optimal input/output and NN structure have been investigated. Field tests show that the proposed hybrid methods can dramatically improve geo-referenced solutions during GPS outages.

Veículos autônomos são objeto de crescente estudo em todo o mundo. Face à Engenharia de Transportes, é tema que deve provocar uma revolução nas próximas décadas, pois é concreta a tendência ao uso destes veículos na sociedade. Podem se citar como grandes beneficiados a segurança, a logística, o fluxo de trânsito, o meio ambiente e também os portadores de deficiências. Com o objetivo de fazer um veículo atingir um ponto com coordenadas conhecidas de forma autônoma, uma plataforma veicular terrestre em escala foi utilizada, a qual recebeu um sistema computacional micro controlado e tecnologias para proporcionar mobilidade através de motores elétricos para tração e servo-motores para direcionamento; posicionamento por satélite através de receptor GNSS e bússola eletrônica para orientação; sensoriamento por ultra-som para evitar colisões; e comunicação sem fio, a fim de se realizar remotamente monitoramento e instrução em tempo real através de um aplicativo para computador pessoal (PC). Foi desenvolvido um algoritmo de navegação que, fazendo uso dos recursos disponíveis, proporcionou autonomia ao veículo, de forma a navegar para pontos com coordenadas conhecidas sem controle humano. Os testes realizados visaram avaliar a capacidade de autonomia do veículo, a trajetória de navegação realizada e a acurácia de chegada aos pontos de destino. O veículo foi capaz de atingir os pontos em todos os testes realizados, sendo considerado funcional seu algoritmo de navegação e também os sistemas de mobilidade, posicionamento, sensoriamento e comunicação. / Autonomous vehicles are an on growing research target around the world. Face to Transports Engineering, it is a subject which is expected to make a revolution on the next decades. The great benefits are on security, logistic, traffic flow, environment and handicap. With the goal to make a vehicle navigate autonomously to known geodesics coordinates, a reduced scale terrestrial vehicular platform was used. This platform received a microcontrolled computational system and technologies to give it mobility, through electrical motors for traction and servo-motors for direction; satellite positioning, through a GNSS receiver and magnetic compass for orientation; ultrasound sensing in order to avoid collision; and wireless communication, in order to do remote monitoring and instruction at real time through a PC application. It was developed a navigation algorithm which, from the available resources, gave autonomy to the vehicle, in order to navigate to known geodesics coordinates without human control. The test set was intended to evaluate the autonomy capacity of the vehicle, the navigation trajectory that was done and the arrival accuracy to the destination points. The vehicle reached the destination points on all tests done, being evaluated as functional its navigation algorithm and also the mobility, positioning, sensing and communication systems.

Cette thèse a pour objet l'étude d'algorithmes pour des applications de réalité visuellement augmentée. Plusieurs besoins existent pour de telles applications, qui sont traités en tenant compte de la contrainte d'indistinguabilité de la profondeur et du mouvement linéaire dans le cas de l'utilisation de systèmes monoculaires. Pour insérer en temps réel de manière réaliste des objets virtuels dans des images acquises dans un environnement arbitraire et inconnu, il est non seulement nécessaire d'avoir une perception 3D de cet environnement à chaque instant, mais également d'y localiser précisément la caméra. Pour le premier besoin, on fait l'hypothèse d'une dynamique de la caméra connue, pour le second on suppose que la profondeur est donnée en entrée: ces deux hypothèses sont réalisables en pratique. Les deux problèmes sont posés dans lecontexte d'un modèle de caméra sphérique, ce qui permet d'obtenir des équations de mouvement invariantes par rotation pour l'intensité lumineuse comme pour la profondeur. L'observabilité théorique de ces problèmes est étudiée à l'aide d'outils de géométrie différentielle sur la sphère unité Riemanienne. Une implémentation pratique est présentée: les résultats expérimentauxmontrent qu'il est possible de localiser une caméra dans un environnement inconnu tout en cartographiant précisément cet environnement. / The purpose of this thesis is to study algorithms for visual augmented reality. Different requirements of such an application are addressed, with the constraint that the use of a monocular system makes depth and linear motion indistinguishable. The real-time realistic insertion of virtual objects in images of a real arbitrary environment yields the need for a dense Threedimensional (3D) perception of this environment on one hand, and a precise localization of the camera on the other hand. The first requirement is studied under an assumption of known dynamics, and the second under the assumption of known depth: both assumptions are practically realizable. Both problems are posed in the context of a spherical camera model, which yields SO(3)-invariant dynamical equations for light intensity and depth. The study of theoreticalobservability requires differential geometry tools for the Riemannian unit sphere. Practical implementation on a system is presented and experimental results demonstrate the ability to localize a camera in a unknown environment while precisely mapping this environment.

The work focuses on the application of satellite navigation systems in the different transport fields. It describes the general principle of operation of navigation systems, as well as its history and development. The following section describes the current navigation systems, the principle of operation, architecture and services. A separate chapter is devoted to being the European Galileo system and its services. Practical work deals with the applications according to the navigation systems across different transport sectors. In addition to the transport sector are given applications in other areas of human activity. The main part is devoted to the application in the monitoring and management of the company's fleet. Part of the analysis is the calculation of the efficiency of this investment.