ABSTRACT LOCAL MODELING OF THE IONOSPHERIC VERTICAL TOTAL ELECTRON CONTENT (VTEC) USING PARTICLE FILTER Aghakarimi, Armin M.Sc., Department of Geodetic and Geographic Information Technologies Supervisor: Prof. Dr. Mahmut Onur Karslioglu September 2012, 98 pages Ionosphere modeling is an important field of current studies because of its influences on the propagation of the electromagnetic signals. Among the various methods of obtaining ionospheric information, Global Positioning System (GPS) is the most prominent one because of extensive stations distributed all over the world. There are several studies in the literature related to the modeling of the ionosphere in terms of Total Electron Content (TEC). However, most of these studies investigate the ionosphere in the global and regional scales. On the other hand, complex dynamic of the ionosphere requires further studies in the local structure of the TEC distribution. In this work, Particle filter has been used for the investigation of local character of the ionosphere VTEC. Besides, standard Kalman filter as an effective method for optimal state estimation is applied to the same data sets to compare the corresponding results with results of Particle filter. The comparison shows that Particle filter indicates better performance than the standard Kalman filter especially during the geomagnetic storm. MATLAB&copy / R2011 software has been used for programing all processes and algorithms of the study.

We consider sensor data scheduling for remote state estimation. Due to constrained communication energy and bandwidth, a sensor needs to decide whether it should send the measurement to a remote estimator for further processing. We propose an event-based sensor data scheduler for linear systems and derive the corresponding minimum squared error estimator. By selecting an appropriate eventtriggering threshold, we illustrate how to achieve a desired balance between the sensor-to-estimator communication rate and the estimation quality. Simulation examples are provided to demonstrate the theory. / <p>QC 20130318</p>

Condition monitoring of hydraulic systems is an area that has grown substantially in the last few decades. This thesis presents a scheme that automatically generates the fault symptoms by on-line processing of raw sensor data from a real test rig. The main purposes of implementing condition monitoring in hydraulic systems are to increase productivity, decrease maintenance costs and increase safety. Since such systems are widely used in industry and becoming more complex in function, reliability of the systems must be supported by an efficient monitoring and maintenance scheme. This work proposes an accurate state space model together with a novel model-based fault diagnosis methodology. The test rig has been fabricated in the Process Automation and Robotics Laboratory at UBC. First, a state space model of the system is derived. The parameters of the model are obtained through either experiments or direct measurements and manufacturer specifications. To validate the model, the simulated and measured states are compared. The results show that under normal operating conditions the simulation program and real system produce similar state trajectories. For the validated model, a condition monitoring scheme based on the Unscented Kalman Filter (UKF) is developed. In simulations, both measurement and process noises are considered. The results show that the algorithm estimates the iii system states with acceptable residual errors. Therefore, the structure is verified to be employed as the fault diagnosis scheme. Five types of faults are investigated in this thesis: loss of load, dynamic friction load, the internal leakage between the two hydraulic cylinder chambers, and the external leakage at either side of the actuator. Also, for each leakage scenario, three levels of leakage are investigated in the tests. The developed UKF-based fault monitoring scheme is tested on the practical system while different fault scenarios are singly introduced to the system. A sinusoidal reference signal is used for the actuator displacement. To diagnose the occurred fault in real time, three criteria, namely residual moving average of the errors, chamber pressures, and actuator characteristics, are considered. Based on the presented experimental results and discussions, the proposed scheme can accurately diagnose the occurred faults.

A new algorithm is proposed that smoothly integrates the nonlinear estimation of the attitude quaternion using Davenport's q-method and the estimation of non-attitude states within the framework of an extended Kalman filter. A modification to the q-method and associated covariance analysis is derived with the inclusion of an a priori attitude estimate. The non-attitude states are updated from the nonlinear attitude estimate based on linear optimal Kalman filter techniques. The proposed filter is compared to existing methods and is shown to be equivalent to second-order in the attitude update and exactly equivalent in the non-attitude state update with the Sequential Optimal Attitude Recursion filter. Monte Carlo analysis is used in numerical simulations to demonstrate the validity of the proposed approach. This filter successfully estimates the nonlinear attitude and non-attitude states in a single Kalman filter without the need for iterations.

In this thesis we consider two combinatorial optimization problems that relate to the field of persistent monitoring. In the first part, we extend the classic problem of finding the maximum weight Hamiltonian cycle in a graph to the case where the objective is a submodular function of the edges. We consider a greedy algorithm and a 2-matching based algorithm, and we show that they have approximation factors of 1/2+κ and max{2/(3(2+κ)),(2/3)(1-κ)} respectively, where κ is the curvature of the submodular function. Both algorithms require a number of calls to the submodular function that is cubic to the number of vertices in the graph. We then present a method to solve a multi-objective optimization consisting of both additive edge costs and submodular edge rewards. We provide simulation results to empirically evaluate the performance of the algorithms. Finally, we demonstrate an application in monitoring an environment using an autonomous mobile sensor, where the sensing reward is related to the entropy reduction of a given a set of measurements. In the second part, we study the problem of selecting sensors to obtain the most accurate state estimate of a linear system. The estimator is taken to be a Kalman filter and we attempt to optimize the a posteriori error covariance. For a finite time horizon, we show that, under certain restrictive conditions, the problem can be phrased as a submodular function optimization and that a greedy approach yields a 1-1/(e^(1-1/e))-approximation. Next, for an infinite time horizon, we characterize the exact conditions for the existence of a schedule with bounded estimation error covariance. We then present a scheduling algorithm that guarantees that the error covariance will be bounded and that the error will die out exponentially for any detectable LTI system. Simulations are provided to compare the performance of the algorithm against other known techniques.

The object with this project is to develop a more accurate way to measure the level in the fuel tank in Scaniavehicles. The level should be displayed for the driver and a warning system be implemented to make the driveraware if the fuel level is too low. Furthermore a main goal is to develop an estimation of the distance that thevehicle could travel before refueling is needed.The fuel level estimation system is modeled using Matlab Simulink and simulated with measurement datacollected from real driving scenarios. After evaluating the system it is implemented in one of the electricalcontrol units located on a test vehicle which communicates with other systems. After implementation more testsare performed with the test vehicle to verify that the same functionality achieved during simulations is achievedusing the system implemented in a vehicle.The fuel level estimated with a KF (Kalman filter) that uses fuel consumption and level measurement results ingood performance. A more stable level estimate is achieved and a negative elevation of the estimate most of thetime, as a result of fuel use. Compared to the method Scania vehicles estimate their fuel level with today thenew level estimate is more steady and not that easily affected by fuel movements. The KF is more demanding interms of memory allocation, processor speed and inputs needed, which has to be considered when comparingboth methods. Another disadvantage with the KF is that it is dependent on the samples from the fuel levelsensor to get an initial estimate during startup.Furthermore the KF is easily expanded with more inputs that use information from other sensors on other parts of the vehicle.

In this thesis report, some of the most promising techniques, in the field of intelligent vehicles and mobile robotics, for detection and tracking of moving objects in an indoor environment are investigated. Kalman filter (KF), extended Kalman filter (EKF), and particle filters (PF) based techniques for the tracking of people are implemented and evaluated. A heuristic method is then proposed to improve the performance of the EKF based tracking in situations where moving objects are hidden by obstacles. The proposed method is based on points of maximum uncertainty (PMU) in occlusion situations and its complexity and accuracy is compared with PF method. The EKF, PF and PMU based methods are examined and compared using experimental data which are extracted by a laser range finder in an indoor environment with predefined hinders and people as the moving objects.

With an increasing traffic intensity the demands on vehicular safety is higher than ever before. Active safety systems that have been developed recent years are a response to that. In this master thesis Sensor Fusion is used to combine information from a laser scanner and a microwave radar in order to get more information about the surroundings in front of a vehicle. The Extended Kalman Filter method has been used to fuse the information from the sensors. The process model consists partly of a Constant Turn model to describe the motion of the ego vehicle as well as a tracked object. These individual motions are then put together in a framework for spatial relationships to describe the relationship between them. Two measurement models have been used to describe the two sensors. They have been derived from a general sensor model. This filter approach has been used to estimate the position and orientation of an object relative the ego vehicle. Also velocity, yaw rate and the width of the object have been estimated. The filter has been implemented and simulated in Matlab. The data that has been recorded and used in this work is coming from a scenario where the ego vehicle is following an object in a quite straight line. Where the ego vehicle is a truck and the object is a bus. One important conclusion from this work is that the filter is sensitive to the number of laser beams that hits the object of interest. No qualitative validation has been made though.

This thesis aims the problem of underwater vehicle positioning. The vehicle usedwas a Saab Seaeye Falcon which was equipped with a Doppler Velocity Log(DVL)manufactured by RD Instruments and an inertial measurement unit (IMU) fromXsense. During the work several different Extended Kalman Filter (EKF) havebeen tested both with a hydrodynamic model of the vehicle and a model withconstant acceleration and constant angular velocity. The filters were tested withdata from test runs in lake Vättern. The EKF with constant acceleration andconstant angular velocity appeared to be the better one. The misalignment of thesensors were also tried to be estimated but with poor result.

In this report, mainly three different problems are considered. The first problem considered is how to filter position data of vehicles. To do so the vehicles have to be tracked. This is done with Kalman filters. The second problem considered is how to control a camera to keep a vehicle in the center of the image, under three different conditions. This is mainly solved with a Kalman filter. The last problem considered is how to use the color of the vehicles to make classification of them more robust. Some suggestions on how this might be done are given. However, no really good method to do this has been found. / Den här rapporten behandlar huvudsakligen tre olika problem. Det första problemet är hur man ska filtrera fordons positions data. För att göra detta måste fordonen följas. Detta är gjort med ett Kalmanfilter. Det andra problemet var att styra en kamera så att ett givet fordon ligger mitt i bild, tre olika förhallånde har betraktats. Detta löstes huvudsakligen med ett Kalmanfilter. Det sista problemet var hur man ska använda fordonens färg så att man får säkrare klassificering av dem. Några förslag på hur detta kan göras ges, men ingen riktigt bra metod har hittats.