Inertial Measurement Units, the main component of a navigation system, are used in several systems today. IMU&rsquo / s main components, gyroscopes and accelerometers, can be produced at a lower cost and higher quantity. Together with the decrease in the production cost of sensors it is observed that the performances of these sensors are getting worse. In order to improve the performance of an IMU, the error compensation algorithms came into question and several algorithms have been designed. Inertial sensors contain two main types of errors which are deterministic errors like scale factor, bias, misalignment and stochastic errors such as bias instability and scale factor instability. Deterministic errors are the main part of error compensation algorithms. This thesis study explains the methodology of how the deterministic errors are defined by 27 state static and 60 state dynamic rate table calibration test data and how those errors are used in the error compensation model. In addition, the stochastic error parameters, gyroscope and bias instability, are also modeled with Gauss Markov Model and instant sensor bias instability values are estimated by Kalman Filter algorithm. Therefore, accelerometer and gyroscope bias instability can be compensated in real time. In conclusion, this thesis study explores how the IMU performance is improved by compensating the deterministic end stochastic errors. The simulation results are supported by a real IMU test data.

Short range thrusting projectiles are one of the various threats against armored vehicles and helicopters on the battlefield. Developing a countermeasure for this kind of projectiles is very crucial since they are vast in number and easy to operate on the battlefield. A countermeasure may consist of fire point prediction of the projectile and attack the launcher of it, or it may be the impact point prediction of the projectile and apply a hard-kill counter measure on its way to the ally target. For both of the countermeasure concepts, dynamics and parameters of the projectile must be estimated precisely. In this thesis, dynamic models for thrusting and ballistic flight modes of thrusting projectile are obtained. Three different tracking filters are suggested for precise tracking of the projectiles and their estimation performances are compared. These filters are the Extended Kalman Filter (EKF), the Particle Filter (PF) and the Marginalized Particle Filter (MPF).

This dissertation explores both the estimation of various parameters from a multiple antenna GPS receiver, which is used as an attitude sensor, and attitude determination using GPS-like Pseudolite signals. To use a multiple antenna GPS receiver as an attitude sensor, parameters such as baselines, integer ambiguities, line biases, and attitude, should be resolved beforehand. Also, due to a cycle slip problem a subsystem to correct this problem should be implemented. All of these tasks are called a self survey. A new algorithm to estimate these parameters from a GPS receiver is developed usingnonlinear batch filteringmethods.For convergence issues, both the nolinear least squares (NLS) and Levenberg-Marquardt (LM) methods are applied in the estimation.Acomparison ofthe NLSand LMmethods shows that the convergence of the LM method for the large initial errors is more robust than that of the NLS. In the proximity of the International Space Station (ISS), Pseudolite signals replace the GPSsignals since almostallsignals are blocked.Since the Pseudolite signals have spherical wavefronts, a new observation model should be applied. A nonlinear predictive filter, an extended Kalman filter (EKF), and an unscented filter (UF) are developed and compared using Pseudolite signals. A nonlinear predictive filter can provide a deterministic solution; however, it cannot be used for the moving case. Instead, the EKF or the UF can be used with the angular rate measurements. A comparison of EKF and UF shows that the convergence of the UF for the large initial errors is more robust than that of the EKF. Also, an alternative global navigation constellation is presented by using the Flower Constellation (FC) scheme. A comparison of FC global navigation constellation and other GPS constellations, U.S. GPS, Galileo, and GLONASS, shows that position and attitude errors of the FC constellation are smaller that those of the others.

<p>This thesis contains the development of a deformation monitoring software based on undifferenced GPS observations. Software like this can be used in alarm systems placed in areas where the earth is unstable. Systems like this can be used in areas where people are in risk of getting hurt, like in earthquake zones or in land slide areas, but they can also be useful when monitoring the movements in buildings, bridges and other artefacts.</p><p>The main hypotheses that are tested are whether it is possible to detect deformations with undifferenced observations and if it is possible to reach the same accuracy in this mode as when working in a traditional mode where the observations are differenced.</p><p>The development of a deformation monitoring software based on undifferenced GPS observations is presented. A complete mathematical model is given as well as implementation details. The software is developed in Matlab together with a GPS observation simulator. The simulator is mainly used for debugging purposes.</p><p>The developed software is tested with both simulated and real observations. Results from tests with simulated observations show that it is possible to detect deformations in the order of a few millimetres with the software. Calculations with real observations give the same results. Further, the result from calculations in static mode indicates that the commercial software and the undifferenced software diverge a few millimetres, which probably depends on different implementations of the tropospheric corrections. In kinematic mode the standard deviation is about 1 millimetre larger in the undifferenced mode than in the double differenced mode. An initial test with different observation weighting procedures indicates that there is a lot of potential to improve the result by applying correct weights to the observations. This is one of the aims in the future work within this project.</p><p>This thesis are sponsored by the Swedish Research Council for Enviroment, Agricultural Sciences and Spatial Planning, FORMAS within the framework “Monitoring of construction and detection of movements by GPS ref no. 2002-1257"</p>

<p>At Flight and Systems test Saab AB, a post-processing software is used to process GPS data. A new software by the name GrafNav has been purchased and the purpose of this master thesis therefore became, partly to make a judgment regarding GrafNav’s ability to estimate position, velocity and accuracy, partly to if needed improve the estimate and finally find one or several methods to estimate the position and velocity accuracy. </p><p>The judgment of GrafNav was performed partly by a comparison to the former post-processing software (PNAV) and partly by a comparison to the airplane’s inertial navigation system (INS). The experiments showed that GrafNav’s ability to estimate the position is comparable with PNAV:s, but its capacity to estimate the velocity is considerably worse. The velocity estimate even showed a more noisy behavior than the original velocity from the receiver. More effort is needed to judge GrafNav’s ability to estimate the accuracy thru its quality signals. </p><p>A few trials were made to improve the velocity estimate thru Kalman filtering (Rauch-Tung-Striebel smoothing). The filtering was first made using only the position data from GrafNav as measurements and afterwards both position and velocity data from GrafNav was used. The outcome of the Kalman filtering showed that the best result is obtained when solely position data is used and that the estimate in general is comparable with PNAV:s estimate, but considerable big deviations is obtained in conjunction to interruptions in position data. More over, is more effort needed using both position and velocity data when performing the smoothing and also replacing the stationary Kalman filter with an adaptive filter. </p><p>Finally a method was brought out to estimate the position precision and a method to estimate the velocity accuracy. Both methods use the INS velocity to perform an estimation.</p>

<p>Navigation with unmanned aerial vehicles (UAVs) requires good knowledge of the current position and other states. A UAV navigation system often uses GPS and inertial sensors in a state estimation solution. If the GPS signal is lost or corrupted state estimation must still be possible and this is where simultaneous localization and mapping (SLAM) provides a solution. SLAM considers the problem of incrementally building a consistent map of a previously unknown environment and simultaneously localize itself within this map, thus a solution does not require position from the GPS receiver.</p><p>This thesis presents a visual feature based SLAM solution using a low resolution video camera, a low-cost inertial measurement unit (IMU) and a barometric pressure sensor. State estimation in made with a extended information filter (EIF) where sparseness in the information matrix is enforced with an approximation.</p><p>An implementation is evaluated on real flight data and compared to a EKF-SLAM solution. Results show that both solutions provide similar estimates but the EIF is over-confident. The sparse structure is exploited, possibly not fully, making the solution nearly linear in time and storage requirements are linear in the number of features which enables evaluation for a longer period of time.</p>

Aktive Magnetlager ermöglichen berührungsfreies Lagern und begrenztes Bewegen von Rotoren. Die für einen stabilen Betrieb notwendige Lageregelung erfordert genaue und schnelle Messsysteme. Die bisher verwendeten Messsysteme erfordern zusätzlichen Bauraum. In dieser Arbeit wird ein integriertes, kapazitives Messsystem für Radiallager vorgestellt, durch das die axiale Baulänge des Rotors reduziert und Dislokationseffekte vermieden werden können. Es wurde dadurch eine höhere Regelungsdynamik erreicht. Außerdem wurde ein erweitertes Kalman-Filter mit nichtlinearer Kraftberechnung implementiert, um die Lageregelung mit verrauschten Messsignalen stabil betreiben zu können. Die Verbesserung der Lageregelung durch das integrierte Messsystem und das Kalman-Filter werden anhand von Versuchsergebnissen verdeutlicht.

Die Dissertation beschreibt Verfahren zur Fusion von Sensordaten am Beispiel eines PreCrash-Systems für Kraftfahrzeuge. Ein PreCrash-System erkennt mit Hilfe von Sensoren, die das Fahrzeugumfeld überwachen, (unvermeidliche) Unfälle wenige hundert Millisekunden vor Beginn des Zusammenstoßes und stellt verschiedene Informationen zur Verfügung, die bei der Aktivierung von Sicherheitseinrichtungen wie Gurtstraffer oder Airbags hilfreich sind. Neben guten Erkennungsleistungen spielt bei einem solchen System vor allem die Eignung für den Einsatz im automobilen Umfeld mit all seinen Anforderungen eine große Rolle. Dies bedeutet zum Beispiel Robustheit gegenüber schwierigen Wetterbedingungen, geringe Anforderungen an die Rechenleistung und auch die Erkennung eines Sensorausfalls. Ebenso stellt die Vielfalt möglicher Objekte mit ihren unterschiedlichen Reflexionseigenschaften und teilweise sehr hohen Relativgeschwindigkeiten eine besondere Herausforderung für ein umfelderkennendes System dar. Nach einführenden Betrachtungen zum Stand der Technik und der Zielstellung, unterschiedliche Sensorik zur Verbesserung der Detektionsleistungen und damit der Robustheit des Gesamtsystems zu fusionieren, beinhaltet die Arbeit eine Beschreibung der Funktionalität "PreCrash", Angaben zu Voraussetzungen und speziellen Umgebungsbedingungen im Fahrzeugbereich, die Einfluss auf die Verfahrensauswahl ausüben und eine Beschreibung der verwendeten Sensorik. Signalverarbeitungsverfahren zur Realisierung eines PreCrash-Systems sind sowohl auf Basis eines Einzelsensorsystems als auch auf Grundlage eines Multisensorsystems ausführlich dokumentiert. Ansätze zur Sensordatenfusion werden gesondert dargestellt und auch Nebenaspekte wie die Erkennung von Sensorausfällen berücksichtigt. Die Arbeit enthält Ergebnisse, die die Erkennungsleistungen mehrerer implementierter Verfahren aufzeigen und die auf realen, mit Hilfe eines Versuchsfahrzeuges aufgenommener Daten basieren.

Techn. Universiẗat, Diss.--Chemnitz, 2005.

This thesis demonstrates how a model-based observer can be applied to estimate the reference pilot expectation that can be achieved with any instrument scanning behavior and established models of vestibular inputs. The MBO, developed by the Georgia Tech Cognitive Engineering Center, is applied here in both simple maneuvers examining spatial disorientation and full Air Traffic Control concepts of operations examining loss of energy state awareness. The computational experiments presented in this thesis examine how different effects (i.e., instrument scan pattern, accuracy of pilot perception of flight display information, and awareness of control surface deflections) can prevent or mitigate the susceptibility to spatial disorientation and loss of energy state awareness, thus setting requirements for intervention and countermeasure designs in terms of the scanning behavior they must foster.