The purpose of this research was to develop an algorithm that could predict ramp meter wait time at metered freeway on-ramps throughout the state of Utah using existing loop detector systems on the ramps. The loop detectors provided data in 60-second increments that include volume, occupancy, and the metering rate. Using these data sources, several ramp meter queue length algorithms were applied; these predicted queue lengths were then converted into wait times by using the metering rate provided by the detector data. A conservation model and several variations of a Kalman filter model generated predicted queue lengths and wait times that were compared to the observed queue lengths. The Vigos model—the model that yielded the best results—provided wait time estimates that were generally within approximately 45 seconds of the observed wait time. This model is simple to implement and can be automated for the Utah Department of Transportation (UDOT) to provide wait time estimates at any metered on-ramp throughout the state.

No description available.

Methods for Direction of Arrival, DOA estimation of multiple objects based on phased arrayantenna technology have many advantages in for example electronic warfare and radarapplications. However, perfect calibration of an antenna array can seldom be achieved. Thepurpose of this report is to study different methods for DOA estimation and how calibration-/modelerrors affect the results. Possible methods for quantifying these kinds of errors using measurement data are suggested. This thesis consists of essentially five parts. The different studies have been carried out using MATLAB simulations as well as theoretical considerations, i.e., calculations. In the first study, examples of the possible performance of four DOA algorithms, MUSIC, TLS-ESPRIT, WSF, and DML are provided. Results are given both with and without applying spatial smoothing. The latter scheme is used for handling correlated, or even coherent, sources. The results show that, for the considered scenarios, MUSIC performs the most consistently well, while the performance of DML is inferior. ESPRIT is well-performing when spatial smoothing is applied and performs the best when the angles of two signals are very close. It has been observed that WSF with weighting matrices for optimal asymptotic performance as well as spatial smoothing applied doesn’t perform well. When applying model errors to the systemin the second study, the corresponding conclusions about the algorithms can be drawn. That separation distance between the angles and that higher SNR results in better estimates are also confirmed. Quantification of certain array errors is also considered using methods inspired by a scheme proposed in the context of nonlinear system identification. The results show that the DOA algorithms are very good at dealing with noise and that the attempted method works well when the model error is like the true signals, but different enough that it is not confused with a problem with more signals. The model error that results in the worst results is when it only affects some ofthe channels in the antenna array. The fourth study explores DOA estimation using extended Kalman filtering and concludes that it is a very good tracker of the angle over time for the considered scenarios. All of this is then applied to measured data, but due to either extensive model error, errors with processing the data, or both, the results are worse than expected. Simulations that try to replicate the measured data results in good angle estimation for the DOA algorithms. The Kalman filter also performs well in simulations.

No description available.

This thesis describes the functional principle of FARN, a novel flight controller for Unmanned Aerial Vehicles (UAVs) designed for mission scenarios that require highly accurate and reliable navigation. The required precision is achieved by combining low-cost inertial sensors and Ultra-Wide Band (UWB) radio ranging with raw and carrier phase observations from the Global Navigation Satellite System (GNSS). The flight controller is developed within the scope of this work regarding the mission requirements of two research projects, and successfully applied under real conditions. FARN includes a GNSS compass that allows a precise heading estimation even in environments where the conventional heading estimation based on a magnetic compass is not reliable. The GNSS compass combines the raw observations of two GNSS receivers with FARN’s real-time capable attitude determination. Thus, especially the deployment of UAVs in Arctic environments within the project for ROBEX is possible despite the weak horizontal component of the Earth’s magnetic field. Additionally, FARN allows centimeter-accurate relative positioning of multiple UAVs in real-time. This enables precise flight maneuvers within a swarm, but also the execution of cooperative tasks in which several UAVs have a common goal or are physically coupled. A drone defense system based on two cooperative drones that act in a coordinated manner and carry a commonly suspended net to capture a potentially dangerous drone in mid-air was developed in conjunction with the project MIDRAS. Within this thesis, both theoretical and practical aspects are covered regarding UAV development with an emphasis on the fields of signal processing, guidance and control, electrical engineering, robotics, computer science, and programming of embedded systems. Furthermore, this work aims to provide a condensed reference for further research in the field of UAVs. The work describes and models the utilized UAV platform, the propulsion system, the electronic design, and the utilized sensors. After establishing mathematical conventions for attitude representation, the actual core of the flight controller, namely the embedded ego-motion estimation and the principle control architecture are outlined. Subsequently, based on basic GNSS navigation algorithms, advanced carrier phase-based methods and their coupling to the ego-motion estimation framework are derived. Additionally, various implementation details and optimization steps of the system are described. The system is successfully deployed and tested within the two projects. After a critical examination and evaluation of the developed system, existing limitations and possible improvements are outlined. / Diese Arbeit beschreibt das Funktionsprinzip von FARN, einer neuartigen Flugsteuerung für unbemannte Luftfahrzeuge (UAVs), die für Missionsszenarien entwickelt wurde, die eine hochgenaue und zuverlässige Navigation erfordern. Die erforderliche Präzision wird erreicht, indem kostengünstige Inertialsensoren und Ultra-Breitband (UWB) basierte Funkreichweitenmessungen mit Roh- und Trägerphasenbeobachtungen des globalen Navigationssatellitensystems (GNSS) kombiniert werden. Die Flugsteuerung wird im Rahmen dieser Arbeit unter Berücksichtigung der Missionsanforderungen zweier Forschungsprojekte entwickelt und unter realen Bedingungen erfolgreich eingesetzt. FARN verfügt über einen GNSS-Kompass, der eine präzise Schätzung des Steuerkurses auch in Umgebungen erlaubt, in denen eine konventionelle Schätzung mit Hilfe eines Magnetkompasses nicht zuverlässig ist. Der GNSS-Kompass kombiniert die Messungen von zwei GNSS-Empfängern mit der echtzeitfähigen Lagebestimmung von FARN. Damit ist insbesondere der Einsatz von UAVs in arktischen Umgebungen im Rahmen des Projektes ROBEX trotz der schwachen horizontalen Komponente des Erdmagnetfeldes möglich. Zusätzlich erlaubt FARN eine zentimetergenaue relative Positionierung mehrerer UAVs in Echtzeit. Dies ermöglicht präzise Flugmanöver innerhalb eines Schwarms, aber auch die Ausführung kooperativer Aufgaben, bei denen mehrere UAVs ein gemeinsames Ziel haben oder physikalisch gekoppelt sind. In Verbindung mit dem Projekt MIDRAS wurde ein Drohnenabwehrsystem entwickelt, das auf zwei kooperativen Drohnen basiert, die koordiniert agieren und ein gemeinsam aufgehängtes Netz tragen, um eine potenziell gefährliche Drohne in der Luft einzufangen. Im Rahmen dieser Arbeit werden sowohl theoretische als auch praktische Aspekte der UAV-Entwicklung behandelt, wobei der Schwerpunkt auf den Bereichen der Signalverarbeitung, der Navigation und der Steuerung, der Elektrotechnik, der Robotik sowie der Informatik und der Programmierung eingebetteter Systeme liegt. Darüber hinaus soll diese Arbeit eine zusammengefasste Referenz für die weitere Drohnenforschung darstellen. Die Arbeit erläutert und modelliert die verwendete UAV-Plattform, das Antriebssystem, das elektronische Design und die eingesetzten Sensoren. Nach der Ausarbeitung mathematischer Konventionen zur Lagedarstellung, wird der eigentliche Kern des Flugreglers erläutert, nämlich die eingebettete Schätzung der Eigenbewegung und die prinzipielle Regelungsarchitektur. Anschließend werden, basierend auf grundlegenden Navigationsalgorithmen, fortgeschrittene trägerphasenbasierte Methoden und deren Zusammenhang mit der Schätzung der Eigenbewegung abgeleitet. Zusätzlich werden verschiedene Implementierungsdetails und Optimierungsschritte des Systems beschrieben. Das System wird innerhalb der beiden Projekte erfolgreich verwendet und getestet. Nach einer kritischen Untersuchung und Bewertung des entwickelten Systems werden bestehende Einschränkungen und mögliche Verbesserungen aufgezeigt.

Longline fishing is a method of fishing that utilizes baited hooks to catch fish in an environmentally friendly way. In order to reduce the number of catch lost while longline fishing, it is of great interest to be able to keep an even tension on the fishing line. This can be done by estimating the speed at the point of interest (POI) at which the fishing line is attached to the boat. Due to the harsh conditionson the seas, it is not recommended to put any sensors directly at that point. The aim of this thesis was to explore whether or not it is possible to estimate the vertical speed at the POI by having sensors measuring linear acceleration and angular velocity at various unknown places in the boat. The sensors were placed at various places in a simulated boat, after which the sensor orientations and positions were calculated using a nonlinear Least Squares method. After the sensors were positioned, an Extended Kalman Filter (EKF) was implemented on each sensor, after which the speed of the POI was calculated as the fused estimate of all EKFs. By changing the number of sensors and their sampling times, the best compromise between accuracy, computational load and number of sensors was found. The results prove that it is fully possible to estimate the vertical speed of the POI using only four 6DOF IMU:s using a sampling time of 50 or 100 ms, depending on how accurate the user wants the estimated positions of the sensors to be. However, there are still many ways in which the method used can be improved to geta better estimate.

For complex satellite missions that rely on agile and high-precision manoeuvres, the low-friction aspect of the space environment is a critical component in understanding the attitude control dynamics of the spacecraft. The Kinesthetic Node and Autonomous Table-Top Emulator (KNATTE) is a three-degree-of-freedom frictionless vehicle that serves as the foundation of a multipurpose platform for real-time spacecraft hardware-in-the-loop experiments, and allows emulation of these conditions in two dimensions with the purpose of validating various guidance, navigation, and control algorithms. The data acquisition of the vehicle depends on a computer vision system (CVS) that yields position and attitude data, but also suffers from unpredictable blackout events. To complement such measurements, KNATTE incorporates an inertial measurement unit (IMU) that yields accelerometer, gyroscope, and magnetometer data. This study describes a multisensor data fusion approach to obtain accurate attitude information by combining the measurements from the CVS and the IMU using nonlinear Kalman filter algorithms. To do this, the data fusion algorithms are developed and tested in a Matlab/Simulink environment. After that, the algorithms are adapted to the KNATTE platform and their performance is confirmed in various conditions. Through this work, the accuracy and efficiency of the approach can be checked by numerical simulation and real-time experiments. In addition, the quality of the CVS measurements are further improved by the introduction of a neural network to the image processing pipeline of the original system.

Aircraft flight test data are processed by optimal estimation programs to estimate the aircraft state trajectory (3 DOF) and to identify the unknown parameters, including constant biases and scale factor of the measurement instrumentation system. The methods applied in processing aircraft flight test data are the iterative extended Kalman filter/smoother and fixed-point smoother (IEKFSFPS) method and the two-step estimator (TSE) method. The models of an aircraft flight dynamic system and measurement instrumentation system are established. The principles of IEKFSFPS and TSE methods are derived and summarized, and their algorithms are programmed with MATLAB codes. Several numerical experiments of flight data processing and parameter identification are carried out by using IEKFSFPS and TSE algorithm programs. Comparison and discussion of the simulation results with the two methods are made. The TSE+IEKFSFPS combination method is presented and proven to be effective and practical. Figures and tables of the results are presented.

No description available.

No description available.