There is a high demand for Indoor Positioning Systems (IPS) due to the wide application possibilities. The main issue for localization by wireless signals in an indoor environment is the multipath propagation problem. Multipath propagation occurs when signals reflects and refract from an object, changing the signals characteristics. Today there is no IPS that can balance the cost, accuracy, and complexity. In January 2019 Bluetooth released a new standard, Bluetooth Low Energy (BLE) 5.1, which enables the ability to measure the Angle of Arrival (AoA) of an incoming signal. The purpose of this thesis is to mitigate the disturbances caused by multipath propagation by conducting a case study. This was done by designing systems that combine different positioning techniques with sensor fusion and evaluating them based on power efficiency, execution time and precision. Two wireless localization techniques were evaluated, trilateration and triangulation, which are based on Received Signal Strength Indication (RSSI) and AoA respectively, along with an Inertial Measurement Unit (IMU). An Extended Kalman Filter EKF was used to fuse the sensor data. The system with the best overall performance uses the AoA signals and a multipath mitigation technique. The system with AoA and IMU had a similar performance but has an overall higher complexity due to the added IMU component. The RSSI system could not satisfy the requirement of precision. / Det finns en stor efterfrågan på inomhus positionssystem (IPS) tack vare de många möjliga tillämpningarna. Det största problemet för inomhuslokalisering med trådlösa signaler i inomhusmiljö är flervägsutbredningsproblemet. Felvägsutbredning inträffar när signaler reflekterar och bryts på ett objekt, och ändrar dess karaktär. Idag finns det inga IPS som kan balansera kostnaden, noggrannhetoch komplexitet. I januari 2019, släppte Bluetooth en ny standard, Bluetooth Low Energy (BLE) 5.1, som gör det möjligt att mäta ankomstvinkeln (AoA) för en inkommande signal. Syftet med denna avhandling är att minska störningarna orsakade av flervägsutbredning genom att utföra en fallstudie. Detta gjordes genom att designa system som kombinerar olika positioneringstekniker med sensor fusion och utvärdera dessa med avseende på energieffektivitet, exekveringstid och precision. Två trådlösa lokaliseringstekniker utvärderades, trilaterering och triangulering, som är baserade på mottagen signalstyrka indikation RSSI och AoA respektive, tillsammans med en tröghetssensor (IMU). Ett Utökat Kalman-Filter (EKF) användes för att kombinera data. Systemet med den överlag bästa prestandan använder AoA-signaler och en teknik för att dämpa flervägsutredningen. Systemet med AoA och IMU hade liknande prestanda men den totala komplexiteten ökade med att lägga till en IMU komponent. RSSI systemet var inte tillräckligt tillfredsställande med avseende på precisionskraven.

Wide-area camera networks are becoming more and more common. They have widerange of commercial and military applications from video surveillance to smart home and from traffic monitoring to anti-terrorism. The design of such a camera network is a challenging problem due to the complexity of the environment, self and mutual occlusion of moving objects, diverse sensor properties and a myriad of performance metrics for different applications. In this dissertation, we consider two such challenges: camera planing and camera fusion. Camera planning is to determine the optimal number and placement of cameras for a target cost function. Camera fusion describes the task of combining images collected by heterogenous cameras in the network to extract information pertinent to a target application. I tackle the camera planning problem by developing a new unified framework based on binary integer programming (BIP) to relate the network design parameters and the performance goals of a variety of camera network tasks. Most of the BIP formulations are NP hard problems and various approximate algorithms have been proposed in the literature. In this dissertation, I develop a comprehensive framework in comparing the entire spectrum of approximation algorithms from Greedy, Markov Chain Monte Carlo (MCMC) to various relaxation techniques. The key contribution is to provide not only a generic formulation of the camera planning problem but also novel approaches to adapt the formulation to powerful approximation schemes including Simulated Annealing (SA) and Semi-Definite Program (SDP). The accuracy, efficiency and scalability of each technique are analyzed and compared in depth. Extensive experimental results are provided to illustrate the strength and weakness of each method. The second problem of heterogeneous camera fusion is a very complex problem. Information can be fused at different levels from pixel or voxel to semantic objects, with large variation in accuracy, communication and computation costs. My focus is on the geometric transformation of shapes between objects observed at different camera planes. This so-called the geometric fusion approach usually provides the most reliable fusion approach at the expense of high computation and communication costs. To tackle the complexity, a hierarchy of camera models with different levels of complexity was proposed to balance the effectiveness and efficiency of the camera network operation. Then different calibration and registration methods are proposed for each camera model. At last, I provide two specific examples to demonstrate the effectiveness of the model: 1)a fusion system to improve the segmentation of human body in a camera network consisted of thermal and regular visible light cameras and 2) a view dependent rendering system by combining the information from depth and regular cameras to collecting the scene information and generating new views in real time.

A lane position system and enhancement techniques, for increasing the robustnessand availability of such a system, are investigated. The enhancements areperformed by using additional sensor sources like map data and GPS. The thesiscontains a description of the system, two models of the system and two implementedfilters for the system. The thesis also contains conclusions and results oftheoretical and experimental tests of the increased robustness and availability ofthe system. The system can be integrated with an existing system that investigatesdriver behavior, developed for fatigue. That system was developed in aproject named Drowsi, where among others Volvo Technology participated. / Ett filpositioneringssystem undersöks och förbättringstekniker för ökandet av robusthetoch tillgängligheten av ett sådant system genom att använda ytterligaresensorkällor som kartdata och GPS. Detta examensarbete presenterar beskrivningenav ett system, två modeller och två implementerade filter. Examensarbetetinnehåller också slutsatser och resultat av teoretiska och experimentella testersom plottar och grafer av ökad robusthet och tillgängligheten av systemet. Dettasystem kan bli integrerat med ett framtaget system som tittar på körrelaterat beteendevid trötthet. Systemet är utvecklat i ett projekt kallat Drowsi, där blandandra Volvo Technology deltog.

A key goal for humans and artificial intelligence systems is to develop an accurate and unified picture of the outside world based on the data from any sense(s) that may be available. The availability of multiple senses presents the perceptual system with new opportunities to fulfil this goal, but exploiting these opportunities first requires the solution of two related tasks. The first is how to make the best use of any redundant information from the sensors to produce the most accurate percept of the state of the world. The second is how to interpret the relationship between observations in each modality; for example, the correspondence problem of whether or not they originate from the same source. This thesis investigates these questions using ideal Bayesian observers as the underlying theoretical approach. In particular, the latter correspondence task is treated as a problem of Bayesian model selection or structure inference in Bayesian networks. This approach provides a unified and principled way of representing and understanding the perceptual problems faced by humans and machines and their commonality. In the domain of machine intelligence, we exploit the developed theory for practical benefit, developing a model to represent audio-visual correlations. Unsupervised learning in this model provides automatic calibration and user appearance learning, without human intervention. Inference in the model involves explicit reasoning about the association between latent sources and observations. This provides audio-visual tracking through occlusion with improved accuracy compared to standard techniques. It also provides detection, verification and speech segmentation, ultimately allowing the machine to understand ``who said what, where?'' in multi-party conversations. In the domain of human neuroscience, we show how a variety of recent results in multimodal perception can be understood as the consequence of probabilistic reasoning about the causal structure of multimodal observations. We show this for a localisation task in audio-visual psychophysics, which is very similar to the task solved by our machine learning system. We also use the same theory to understand results from experiments in the completely different paradigm of oddity detection using visual and haptic modalities. These results begin to suggest that the human perceptual system performs -- or at least approximates -- sophisticated probabilistic reasoning about the causal structure of observations under the hood.

International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / The Stanford University Satellite Systems Development Laboratory will flight test a telemetry reengineering experiment on its student-built SAPPHIRE spacecraft. This experiment utilizes solar panel current information and knowledge of panel geometry in order to create a virtual sun sensor that can roughly determine the satellite's sun angle. The Omni-Directional Differential Sun Sensor (ODDSS) algorithm normalizes solar panel currents and differences them to create a quasi-linear signal over a particular sensing region. The specific configuration of the SAPPHIRE spacecraft permits the construction of 24 such regions. The algorithm will account for variations in panel outputs due to battery charging, seasonal fluctuations, solar cell degradation, and albedo affects. Operationally, ODDSS telemetry data will be verified through ground processing and comparison with data derived from SAPPHIRE's infrared sensors and digital camera. The expected sensing accuracy is seven degrees. This paper reviews current progress in the design and integration of the ODDSS algorithm through a discussion of the algorithm's strategy and a presentation of results from hardware testing and software simulation.

In some wireless sensor network applications, multiple sensors can be used to measure the same variable, while differing in their sampling cost, for example in their power requirements. This raises the problem of automatically controlling heterogeneous sensor suites in wireless sensor network applications, in a manner that balances cost and accuracy of sensors. Genetic programming (GP) is applied to this problem, considering two basic approaches. First, a hierarchy of models is constructed, where increasing levels in the hierarchy use sensors of increasing cost. If a model that polls low cost sensors exhibits too much prediction uncertainty, the burden of prediction is automatically transferred to a higher level model using more expensive sensors. Second, models are trained with cost as an optimization objective, called non-hierarchical models, that use conditionals to automatically select sensors based on both cost and accuracy. These approaches are compared in a setting where the available budget for sampling is considered to remain constant, and in a setting where the system is sensitive to a fluctuating budget, for example available battery power. It is showed that in both settings, for increasingly challenging datasets, hierarchical models makes predictions with equivalent accuracy yet lower cost than non-hierarchical models.

With a larger global population and fewer farmers, harvests will have to be larger and easier to manage. By high precision planting, each crop will have the same available area on the field, yielding an even size of the crops which means the whole field can be harvested at the same time. This thesis investigates the possibility for such precision planting in curves. Currently, Väderstads planter collection Tempo, can deliver precision in the centimeter range for speeds up to 20 km/h when driving straight, but not when turning. This thesis makes use of the available sensors on the planters, but also investigates possible improvements by including additional sensors. An Extended Kalman Filter is used to estimate the individual speeds of the planting row units and thus enabling high precision planting for an arbitrary motion. The filter is shown to yield a satisfactory result when using the internal measurement units, the radar speed sensor and the GPS already mounted on the planter. By implementing the filter, a higher precision is obtained compared to using the same global speed for all planting row units.

In this dissertation, we have focused on conflicts that occur due to disagreeing motions in multi-modal localization algorithms. In spite of the recent achievements in robust localization by means of multi-sensor fusion, these algorithms are not applicable to all environments. This is primarily attributed to the following fundamental assumptions: (i) the environment is predominantly stationary, (ii) only ego-motion of the sensor platform exists, and (iii) multiple sensors are always in agreement with each other regarding the observed motion. Recently, studies have shown how to relax the static environment assumption using outlier rejection techniques and dynamic object segmentation. Additionally, to handle non ego-motion, approaches that extend the localization algorithm to multi-body tracking have been studied. However, there has been no attention given to the conditions where multiple sensors contradict each other with regard to the motions observed. Vision based localization has become an attractive approach for both indoor and outdoor applications due to the large information bandwidth provided by images and reduced cost of the cameras used. In order to improve the robustness and overcome the limitations of vision, an Inertial Measurement Unit (IMU) may be used. Even though visual-inertial localization has better accuracy and improved robustness due to the complementary nature of camera and IMU sensor, they are affected by disagreements in motion observations. We term such dynamic situations as environments with motion conflictbecause these are caused when multiple different but self- consistent motions are observed by different sensors. Tightly coupled visual inertial fusion approaches that disregard such challenging situations exhibit drift that can lead to catastrophic errors. We have provided a probabilistic model for motion conflict. Additionally, a novel algorithm to detect and resolve motion conflicts is also presented. Our method to detect motion conflicts is based on per-frame positional estimate discrepancy and per- landmark reprojection errors. Motion conflicts were resolved by eliminating inconsistent IMU and landmark measurements. Finally, a Motion Conflict aware Visual Inertial Odometry (MC- VIO) algorithm that combined both detection and resolution of motion conflict was implemented. Both quantitative and qualitative evaluation of MC-VIO on visually and inertially challenging datasets were obtained. Experimental results indicated that MC-VIO algorithm reduced the absolute trajectory error by 70% and the relative pose error by 34% in scenes with motion conflict, in comparison to the reference VIO algorithm. Motion conflict detection and resolution enables the application of visual inertial localization algorithms to real dynamic environments. This paves the way for articulate object tracking in robotics. It may also find numerous applications in active long term augmented reality.

The widespread use of mobile devices and the rise of Global Navigation Satellite Systems (GNSS) have allowed mobile tracking applications to become very popular and valuable in outdoor environments. However, tracking pedestrians in indoor environments with Global Positioning System (GPS)-based schemes is still very challenging given the lack of enough signals to locate the user. Along with indoor tracking, the ability to recognize pedestrian behavior and activities can lead to considerable growth in location-based applications including pervasive healthcare, leisure and guide services (such as, museum, airports, stores, etc.), and emergency services, among the most important ones. This thesis presents a system for pedestrian tracking and activity recognition in indoor environments using exclusively common off-the-shelf sensors embedded in smartphones (accelerometer, gyroscope, magnetometer and barometer). The proposed system combines the knowledge found in biomechanical patterns of the human body while accomplishing basic activities, such as walking or climbing stairs up and down, along with identifiable signatures that certain indoor locations (such as turns or elevators) introduce on sensing data. The system was implemented and tested on Android-based mobile phones with a fixed phone position. The system provides accurate step detection and count with an error of 3% in flat floor motion traces and 3.33% in stairs. The detection of user changes of direction and altitude are performed with 98.88% and 96.66% accuracy, respectively. In addition, the activity recognition module has an accuracy of 95%. The combination of modules leads to a total tracking error of 90.81% in common human motion indoor displacements.

<p>The market for vertical takeoff and landing unmanned aerial vehicles, VTOL UAVs, is growing rapidly. To reciprocate the demand of VTOL UAVs in offshore applications, CybAero has developed a novel concept for landing on moving objects called MALLS, Mobile Automatic Launch and Landing Station. MALLS can tilt its helipad and is supposed to align to either the horizontal plane with an operator adjusted offset or to the helicopter skids. Doing so, eliminates the gyroscopic forces otherwise induced in the rotordisc as the helicopter is forced to change attitude when the skids align to the ground during landing or when standing on a jolting boat with the rotor spun up. This master’s thesis project is an attempt to get the concept of MALLS closer to a quarter scale implementation. The main focus lies on the development of the measurement methods for achieving the references needed by MALLS, the hori- zontal plane and the plane of the helicopter skids. The control of MALLS is also discussed. The measurement methods developed have been proved by tested implementations or simulations. The theories behind them contain among other things signal filtering, Kalman filtering, sensor fusion and search algorithms. The project have led to that the MALLS prototype can align its helipad to the horizontal plane and that a method for measuring the relative attitude between the helipad and the helicopter skids have been developed. Also suggestions for future improvements are presented.</p>