I detta examensarbete undersöks möjligheten att kunna använda en positioneringsmetod som inte enbart förlitar sig på den uppmätta signalstyrkan. Istället används en metod som bestämmer från vilken vinkel en signal uppkommer ifrån. Den här tekniken kallas för direction-finding. När informationen om signalens vinkel fastställts används den i ett positioningsfilter som uppskattar positionen. Två tillvägagångssätt har använts i den här rapporten, ett där enbart vinkeln används och ett där både signalstyrka och vinkel används.

Simultaneous localisation and mapping (SLAM) is an often used positioning approach in GPS denied indoor environments. This thesis presents a novel method of combining SLAM with autonomous/aided target detection and recognition (ATD/R), which is beneficial for both methods. The method uses physical objects that are recognisable by ATR as unambiguous features in SLAM, while SLAM provides the ATR with better position estimates. The intended application is to improve the positioning of a first responder moving through an indoor environment, where the map offers localisation and simultaneously helps locate people, furniture and potentially dangerous objects like gas cannisters. The developed algorithm, dubbed ATR-SLAM, uses existing methods from different fields such as EKF-SLAM and ATR based on rectangle estimation. Landmarks in the form of 3D point features based on NARF are used in conjunction with identified objects and 3D object models are used to replace landmarks when the same information is used. This leads to a more compact map representation with fewer landmarks, which partly compensates for the introduced cost of the ATR. Experiments performed using point clouds generated from a time-of-flight laser scanner show that ATR-SLAM produces more consistent maps and more robust loop closures than EKF-SLAM using only NARF landmarks.

Increasing demands in areas such as security, surveillance, search and rescue, and communication, has promoted the research and development of unmanned aerial vehicles (UAVs) as such technologies can replace manned flights in dangerous or unfavorable conditions. Lighter-than-air UAVs such as blimps can carry higher payloads and can stay longer in the air compared to typical heavier-than-air UAVs such as aeroplanes or quadrotors. One purpose of this thesis is to develop a sensor suite basis for estimating the position and orientation of a blimp UAV in development with respect to a reference point for safer landing procedures using minimal on-board sensors. While the existing low-cost sensor package, including inertial measurement unit (IMU) and Global Navigation System (GPS) module, could be sufficient to estimate the pose of the blimp to a certain extent, the GPS module is not as precise in the short term, especially for altitude. The proposed system combines GPS and inertial data with information from a grounded infrared (IR) camera. Image frames are processed to identify three IR LEDs located on the UAV and each LED coordinate is estimated using a Perspective-n-Point (PnP) algorithm. Then the results from the PnP algorithm are fused with the GPS, accelerometer and gyroscope measurements using an Extended Kalman Filter (EKF) to get a more accurate estimate of the position and the orientation. Tests were conducted on a simulated blimp using the experimental avionics.

The use and practical applications of small UAV systems has continually grown in the past several years in both the public and private sectors. These UAV systems are used for not only defensive purposes, but for commercial applications such as exterior bridge and home inspections, wildlife/wildfire management and observation, conservation exercises, law-enforcement, radio-repeating operations, and a wide variety of other uses that may not warrant the use, expense, space constraints, or risk of a manned aircraft. This thesis focuses on the design of a fixed pitch multirotor UAV system for use in furthering research projects and facilitating payload data collection from a flying platform without the expense or risk of testing with available larger UAV systems. The design of a multirotor UAV system with a flight control scheme, communication architecture and hardware, electrical architecture and hardware, and mechanical design is presented. An Extended Kalman Filter (EKF) strategy is implemented aboard a developed Inertial Measurement Unit (IMU) to estimate vehicle state. Experiments then validated the estimates from the EKF through a comparative approach between the developed unit and a commercial unit. A nonlinear flight control system is implemented based on an Integral-Backstepping control strategy. The flight control strategy was then fully simulated and exhaustively tested under a variety of external disturbances and initial conditions from a fully dynamic modeled environment. Parameters about the vehicle were experimentally determined to increase the accuracy of the model which would increase the chances of successful flight operations. Flight demonstrations were conducted to evaluate the abilities and performance of the control system, along with testing the interface abilities and reliability between a universal ground control station (UGCS) and the aircraft. Lastly, the model was revisited with the input data from the flight control experiment and the output captured was evaluated against the output of the model system to evaluate effectiveness, reliability, and accuracy of the model. The results of the comparison showed that the computer simulation was accurate in predicting attitude and altitude of the vehicle to that of the realized system. / Master of Science

The most common way to track the position of a vehicle is by using the Global Navigation Satellite System (GNSS). Unfortunately, there are many scenarios where GNSS is inaccessible or provides low precision, and it can therefore be vulnerable to only rely on GNSS. This master's thesis is done in collaboration with the Swedish Defence Research Agency (FOI), who is looking for a solution to this problem. Therefore, this master's thesis develops a system that globally localizes a vehicle in a map, without GNSS. The approach is to combine odometry and the scan registration algorithm iterative closest point (ICP), in an extended Kalman filter (EKF), to provide global position estimates. The ICP algorithm aligns two different sets of data points, referred to as point clouds. In this thesis, one set consists of light detection and ranging (LIDAR) data points collected from a sensor mounted on a vehicle, and the other consists of LIDAR data points collected from an aircraft which forms an elevation map of the area. In the ideal case, the algorithm finds the position on the elevation map where the vehicle collected the data points. For the EKF to function, the uncertainty of ICP must be estimated. Different methods are investigated, which are; unscented transform based covariance, covariance with Hessian, and covariance with correspondences. The result shows that all the methods are too optimistic when estimating the uncertainty. The reason is that none of the methods take all sources of error into account, and it is therefore difficult to correctly capture the uncertainty of ICP. The unscented transform based covariance is the least optimistic, and covariance with correspondences is the most. A second problem investigated in this thesis is how odometry and ICP with an elevation map as reference can be combined to provide a global position estimate. As mentioned, the chosen approach is to implement an EKF which weights the different data sources based on their covariance, to one single estimate. The developed global localization system is evaluated in a real time experiment, where the data is recorded using equipment from FOI. The goal of the experiment is to localize a vehicle while it is driving in different environments, including urban, field and forest environments. The result shows that the performance of the system is viable, and it manages to provide localization within a few meters from ground truth. However, since the ICP covariance estimates are not fully accurate, the performance of the EKF is decreased as it cannot weight the different estimates properly. The ICP algorithm used in the system has a lot of flaws. The worst is that it easily converges to incorrect solutions, in other words that it estimates the wrong position of the vehicle. How this risk can be decreased is also investigated in this thesis. A method that decreases this risk drastically, and makes the viable performance of the system possible, is developed. The approach of the method is to exclude incorrect positions by removing a large amount of points from the point clouds, and keeping the most informative. By only utilizing the most informative data points in the point cloud, global positions with high accuracy are achieved.

Control and diagnosis of complex systems demand accurate knowledge of certain quantities to be able to control the system efficiently and also to detect small errors. Physical sensors are expensive and some quantities are hard or even impossible to measure with physical sensors. This has made model-based estimation an attractive alternative. Model-based estimators are sensitive to errors in the model and since the model complexity needs to be kept low, the accuracy of the models becomes limited. Further, modeling is hard and time consuming and it is desirable to design robust estimators based on existing models. An experimental investigation shows that the model deficiencies in engine applications often are stationary errors while the dynamics of the engine is well described by the model equations. This together with fairly frequent appearance of sensor offsets have led to a demand for systematic ways of handling stationary errors, also called bias, in both models and sensors. In the thesis systematic design methods for reducing bias in estimators are developed. The methods utilize a default model and measurement data. In the first method, a low order description of the model deficiencies is estimated from the default model and measurement data, resulting in an automatic model augmentation. The idea is then to use the augmented model for estimator design, yielding reduced stationary estimation errors compared to an estimator based on the default model. Three main results are: a characterization of possible model augmentations from observability perspectives, an analysis of what augmentations that are possible to estimate from measurement data, and a robustness analysis with respect to noise and model uncertainty. An important step is how the bias is modeled, and two ways of describing the bias are introduced. The first is a random walk and the second is a parameterization of the bias. The latter can be viewed as an extension of the first and utilizes a parameterized function that describes the bias as a function of the operating point of the system. The parameters, rather than the bias, are now modeled as random walks, which eliminates the trade-off between noise suppression in the parameter convergence and rapid change of the offset in transients. This is achieved by storing information about the bias in different operating points. A direct application for the parameterized bias is the adaptation algorithms that are commonly used in engine control systems. The methods are applied to measurement data from a heavy duty diesel engine. A first order model augmentation is found for a third order model and by modeling the bias as a random walk, an estimation error reduction of 50 % is achieved for a European transient cycle. By instead letting a parameterized function describe the bias, simulation results indicate similar, or better, improvements and increased robustness.

Classical GNSS based positioning has accuracy limitations due to many sources oferror. The error sources range from clock errors and orbit errors toerrors due to variations in atmospheric propagation delays. One way to improve GNSSpositioning is to generate real time corrections using a GNSS reference network.The corrections can then be distributed through the mobilenetwork and be delivered in real time to the device that should position itself.  This thesis aims to develop a positioning engine utilizing statespace representation corrections (SSR). The thesis also has the goal to develop methods for combiningpseudorange measurements with carrier-phase measurements, in the case when SSR correctionsare used. The static and dynamic performance ofthe positioning engine will be evaluated. Also, the  SSR correction format itself, willalso be evaluated and different levels of SSR corrections will be compared. The proposed combined positioning engine uses SSR correctionsand single-difference measurements. Through this, all majorerror sources on the satellite side, device side and in the atmosphere, are removedexcept for an integer ambiguity in the carrier phase measurement. This ambiguityis handled by tracking the GNSS receiver's position along with the integerambiguities in an extended Kalman filter (EKF). Experiments show that usingreal-time SSR corrections leads to a significant improvement in global absolutepositioning for simple GNSS receivers using only a single measurement frequencyand only using pseudorange measurements. For a more advanced receiver capable ofcarrier phase measurements, experiments together with simulation resultsshow that using the proposed combined positioning engine, improves the positioningperformance even further.

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Camera based navigation is getting more and more popular and is the often the cornerstone in Augmented and Virtual Reality. However, navigation systems using camera are less accurate during fast movements and the systems are often resource intensive in terms of CPU and battery consumption. Also, the image processing algorithms introduce latencies in the systems, causing the information of the current position to be delayed. This thesis investigates if a camera and an IMU can be fused in a loosely coupled Extended Kalman Filter to reduce these problems. An IMU introduces unnoticeable latencies and the performance of the IMU is not affected by fast movements. For accurate tracking using an IMU it is important to estimate the bias correctly. Thus, a new method was used in a calibration step to see if it could improve the result. Also, a method to estimate the relative position and orientation between the camera and IMU is evaluated. The filter shows promising results estimating the orientation. The filter can estimate the orientation without latencies and can also offer accurate tracking during fast rotation when the camera is not able to estimate the orientation. However, the position is much harder and no performance gain could be seen. Some methods that are likely to improve the tracking are discussed and suggested as future work.