In this master thesis, a real-time control system that stabilizes the rotational rates of a tri-copter, has been studied. The tricopter is a rotorcraft with three rotors. The tricopter has been modelled and identified, using system identification algorithms. The model has been used in a Kalman filter to estimate the state of the system and for design ofa model based controller. The control approach used in this thesis is a model predictive controller, which is a multi-variable controller that uses a quadratic optimization problem to compute the optimal con-trol signal. The problem is solved subject to a linear model of the system and the physicallimitations of the system. Two different types of algorithms that solves the MPC problem have been studied. These are explicit MPC and the fast gradient method. Explicit MPC is a pre-computed solution to the problem, while the fast gradient method is an online solution. The algorithms have been simulated with the Kalman filter and were implemented on themicrocontroller of the tricopter.

A general automotive navigation system is a satellite navigation system designed for use inautomobiles. It typically uses GPS to acquire position data to locate the user on a road in the unit's map database. However, due to recent improvements in the performance of small and lightweight micro-machined electromechanical systems (MEMS) inertial sensors have made the application of inertial techniques to such problems, possible. This has resulted in an increased interest in the topic of inertial navigation. In location tracking system, sensors are used either individually or in conjunction like in data fusion. However, still they remain noisy, and so there is a need to measure maximum data and then make an efficient system that can remove the noise from data and provide a better estimate. The task of this thesis work was to take data from two sensors, and use an estimation technique toprovide an accurate estimate of the true location. The proposed sensors were an accelerometer and a GPS device. This thesis however deals with using accelerometer sensor and using estimation scheme, Kalman filter. The thesis report presents an insight to both the proposed sensors and different estimation techniques. Within the scope of the work, the task was performed using simulation software Matlab. Kalman filter’s efficiency was examined using different noise levels.

A general automotive navigation system is a satellite navigation system designed for use in automobiles. It typically uses GPS to acquire position data to locate the user on a road in the unit's map database. However, due to recent improvements in the performance of small and light weight micro-machined electromechanical systems (MEMS) inertial sensors have made the application ofinertial techniques to such problems, possible. This has resulted in an increased interest in the topic of inertial navigation. In location tracking system, sensors are used either individually or in conjunction like in data fusion.However, still they remain noisy, and so there is a need to measure maximum data and then make an efficient system that can remove the noise from data and provide a better estimate.The task of this thesis work was to take data from two sensors, and use an estimation technique to provide an accurate estimate of the true location. The proposed sensors were an accelerometer and aGPS device. This thesis however deals with using accelerometer sensor and using estimation scheme, Kalman filter. This thesis report presents an insight to both the proposed sensors and different estimation techniques.Within the scope of the work, the task was performed using simulation software Matlab. Kalman filter’s efficiency was examined using different noise levels.

This master thesis studies distributed navigation which isa function implemented in a future network based combat information system to improve the accuracy in navigation for combat vehicles in a mechanized battalion, above all in the event of loss of GPS. In the event of loss of the GPS the vehicles obtain dead reckoning performance through the backup system that consists of an odometer and a magnetic compass. Dead reckoning means a drift in the position that makes the accuracy in the navigation worse. The distributed navigation function uses position and navigation data with measurements between the vehicles to estimate the errors and uncertainties in positions, which are used to improve the accuracy in position for the vehicles. To investigate and demonstrate distributed navigation, a simulation environment has been produced in Matlab. The environment is general so different navigation systems can be used and studied and also dynamical so a further development is possible. The simulation environment has been used to investigate and evaluate an implementation of distributed navigation. The implementation has been made using a central filter where fusion takes place of all navigation data and measurements. This filter has been realized with help of Kalman filter theory, in which all vehicles are put together in a state space model. Simulations have been performed for different scenarios and the result of these show that the drift in position is avoided.

Multiple Hypothesis Tracking (MHT) is generally the preferred data association technique for tracking targets in clutter and with missed detections due to its increased accuracy over conventional single-scan techniques such as Nearest Neighbor (NN) and Probabilistic Data Association (PDA). However, this improved accuracy comes at the price of greater complexity. Sequential K-best MHT is a simple implementation of MHT that attempts to achieve the accuracy of multiple hypothesis tracking with some of the simplicity of single-frame methods. Our first major objective is to determine under what general conditions Sequential K-best data association is preferable to Probabilistic Data Association. Both methods are implemented for a single-target, single-sensor scenario in two spatial dimensions. Using the track loss ratio as our primary performance metric, we compare the two methods under varying false alarm densities and missed-detection probabilities. Upon implementing a single-target Sequential K-best MHT tracker, a fundamental problem was observed in which the tracks coalesce. The second major thrust of this research is to compare different approaches to resolve this issue. Several methods to detect track coalescence, mostly based on the Mahalanobis and Kullback-Leibler distances, are presented and compared.

Spinning targets are usually observed in videos. The targets may sometimes appear as mobile targets at the same time. The targets become mobile spinning targets. Tracking a single point on a target is easier than tracking the whole target. We use a characteristic point on the target to estimate the interested parameters, such as angular velocity, virtual rotation center and moving velocity. Among these parameters, virtual rotation center does not spin, therefore it can be used to represent the position of the target. Traditionally, extended Kalman filter (EKF), unscented Kalman filter (UKF) and particle filter (PF) are choices for solving the nonlinear problems, but some problems exist. Linearization errors cause that EKF cannot accurately estimate the angular velocity. UKF and PF have high computational complexity. In the thesis, we give angular velocity an initial value. So we can establish a linear dynamic system model to displace the nonlinear model. Then, a new structure is proposed to avoid errors caused by initial value of angular velocity. In the structure, angular velocity is estimated individually and used to correct the initial value by feedback. We try to use fast Fourier transform to estimate angular velocity. But the convergence time of this method is affected by the value of angular velocity, and the direction of angular velocity can not be estimated directly. Therefore, Kalman filter (KF) with pseudo measurement is proposed to estimate the value of angular velocity. The estimator is accurate and has low computational complexity. Once angular velocity is estimated, we can easily predict the virtual rotation center from geometric relationship. In video system, measurements may be quantized and targets may sometimes be obstacled. We fix the measurement equation and use KF to mitigate quantization error. When measurements for the target is missing, the previous state is used to predict the current state. Finally, computer simulations are conducted to verify the effectiveless of the proposed method. The method can work in environments where measurement noise or quantization error exists. The methods can also be applied to different kinds of mobile spinning targets.

Nowadays, the location technology in the field of the Intelligent Transformation System (ITS) is used generally. Most of location devices on the cars are low-cost GPS, however, it¡¦s not enough if we want to combine with the safe algorithm. Hence, we present a suit of vehicle collision-avoidance system which combined location technology with Intersection-agent in this thesis. The system uses vehicle sensors and GPS information to calculate in Extend Kalman Filter, in order to get the optimal location information. Furthermore, Map-Matching algorithm is used to match the vehicle location on the right road. As to the driver¡¦s safety, laser range scanner¡¦s data are used in fuzzy algorithm and calculate the safe distance between cars. In the intersection area where accident happened most, we also combine with Intersection-agent system to enhance safety. When moving objects cross through the intersection area, Intersection-agent system would use laser range scanner to find the moving objects¡¦ position and velocity, judging whether they can pass the intersection safely or not. Once it¡¦s not safe, system would send out warning signal to the drivers to brake cars, also, passing the position information to car location system by wireless RS-232 transceiver, to decrease location error and let vehicle¡¦s location precision more accurate. In brief, this thesis combines with vehicle location, wireless transmission, car following warning system and Intersection-agent. And make sure this system we developed can fit in with traffic requirement in many experiments.

In this thesis, we use the dynamic factor model in state space, proposed by Stock and Watson (1989), to estimate the fluctuations of common factor by using lots of macroeconomic variables. Besides, with the combination of two stage dynamic factor analysis model which is proposed by Aruba et. al (2010), we want to discuss the possibility for the correlation of economic fluctuations across countries to change with different time periods. The thesis verifies the following three conclusions: First, the correlations of the economic fluctuations across countries are significant due to the regional economics. Second, the global or regional common shocks will increase the correlations of the economic fluctuations across countries. Finally, developed countries and emerging countries response differently during the Financial Tsunami from 2008 to 2009.

Precise positioning is crucial for the success of navigation of underwater vehicles. At present, different instruments and methods are available for underwater positioning but few of them are reliable for three-dimensional position sensing of underwater vehicles. Long baseline (LBL) positioning is the standard method for three-dimensional underwater navigation. However, the accuracy of LBL positioning suffers from its own drawback of relatively low update rates. To improve the accuracy in positioning an underwater vehicle, integration of additional sensing measurements in a LBL navigation system is necessary. In this study, numerical simulation and experiment are conducted to investigate the effect of interrogate rate on the accuracy of LBL positioning. Numerical and experimental results show that the longer the interrogate rate, the greater the LBL positioning error. In addition, no reply from a transponder to transceiver interrogation is another major error source in LBL positioning. The experimental result also shows that the accuracy of LBL positioning can be significantly improved by the integration of velocity sensing. Therefore, based on Kalman filter, this study integrates a LBL system with vehicle dynamic model to improve the accuracy of positioning an underwater vehicle. For conducting the positioning experiments, a remotely operated vehicle (ROV) with dedicated Graphic User Interface (GUI) is designed, constructed, and tested. To have a precise motion simulation of ROV, a nonlinear dynamic model of ROV with six degrees of freedom (DOF) is used and its hydrodynamic parameters are identified. Finally, the positioning experiment is run by maneuvering the ROV to move along an ¡§S¡¨ trajectory, and Kalman filter is adopted to propagate the error covariance, to update the measurement errors, and to correct the state equation when the measurements of range, depth, and thruster command are available. The experimental result demonstrates the effectiveness of the integrated LBL system with the ROV dynamic model on the improvement of accuracy of positioning an underwater vehicle.

In order to achieve the storage QoS and power saving, we proposed a fast data migration/transmission scheme and a power saving algorithm for Dataenode management. The fast data migration/ transmission scheme consists of three mechanisms. First, it uses multicast to improve the network bandwidth and solve the I/O and bandwidth bottlenecks. Then, a network coding is used to increase the network throughput and retain high fault tolerance. Third, it uses a user/Dataenode connection management to prevent missing the important message and collocates with CPU & I/O bound scheduling to make data evenly stored in the system. Experimental results show the proposed fast data migration/transmission improves 56% and 85% efficiency in the upload bandwidth and the response time. The proposed power saving algorithm applies the Kalman filter first and then add with the pattern analysis to predict the system workload to adjust the number of Dataenodes dynamically in order to save power. Experimental results show that the proposed power saving algorithm for Dataenode management achieves more than 92.97% accuracy in the workload prediction and improves 52.25% in power consumption with 3.82% error rate.