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.

In this thesis, we study channel estimation in time-varying cooperative network. Since channels vary with time, we insert training blocks periodically to trace channel variation. In this work, we adopt Kalman filter to trace channel variation due to its low complexity. By storing previous channel estimate, Kalman filter simply requires to process next received vectors to update current channel estimate. We use all past observations to estimate current channel state to avoid wasting information. In content of cooperation, we directly estimate effective channel from source through relay to the destination. The reason is that, we separately estimate the source-relay and relay-destination links, relays need extra efforts to estimate the channel and feedback estimates to the destination. It will increase the computational loading on relays, and the feedback channel may suffer channel fading, resulting in more distortion of estimates. Therefore, the destination directly estimate effective channel, using Kalman filter to trace variation. Furthermore, we design pre-coding scheme on relays for forwarding training symbols in order to reduce channel estimation errors and obtain more accurate channel information. To detect data symbols, we need to channel state information over each data block as well. Therefore, estimates over previous training blocks are interpolated to estimate channel over data blocks based on LMMSE criterion. Since estimates over training blocks are obtained from Kalman filter, it consequently improves estimation quality of the channel over the data blocks. The main contributions of the thesis are optimal training design to reduce the estimation error, the estimation based on Kalman filter, and linearly combing the estimates to provide more accurate estimates of the channels over data blocks.

Forecasting is a critical component of project management. Project managers must be able to make reliable predictions about the final duration and cost of projects starting from project inception. Such predictions need to be revised and compared with the project’s objectives to obtain early warnings against potential problems. Therefore, the effectiveness of project controls relies on the capability of project managers to make reliable forecasts in a timely manner. This dissertation focuses on forecasting project schedule progress with probabilistic methods. Currently available methods, for example, the critical path method (CPM) and earned value management (EVM) are deterministic and fail to account for the inherent uncertainty in forecasting and project performance. The objective of this dissertation is to improve the predictive capabilities of project managers by developing probabilistic forecasting methods that integrate all relevant information and uncertainties into consistent forecasts in a mathematically sound procedure usable in practice. In this dissertation, two probabilistic methods, the Kalman filter forecasting method (KFFM) and the Bayesian adaptive forecasting method (BAFM), were developed. The KFFM and the BAFM have the following advantages over the conventional methods: (1) They are probabilistic methods that provide prediction bounds on predictions; (2) They are integrative methods that make better use of the prior performance information available from standard construction management practices and theories; and (3) They provide a systematic way of incorporating measurement errors into forecasting. The accuracy and early warning capacity of the KFFM and the BAFM were also evaluated and compared against the CPM and a state-of-the-art EVM schedule forecasting method. Major conclusions from this research are: (1) The state-of-the-art EVM schedule forecasting method can be used to obtain reliable warnings only after the project performance has stabilized; (2) The CPM is not capable of providing early warnings due to its retrospective nature; (3) The KFFM and the BAFM can and should be used to forecast progress and to obtain reliable early warnings of all projects; and (4) The early warning capacity of forecasting methods should be evaluated and compared in terms of the timeliness and reliability of warning in the context of formal early warning systems.

Nonlinear estimation techniques play an important role for process monitoring since some states and most of the parameters cannot be directly measured. There are many techniques available for nonlinear state and parameter estimation, i.e., extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filtering (PF) and moving horizon estimation (MHE) etc. However, many issues related to the available techniques are to be solved. This dissertation discusses three important techniques in nonlinear estimation, which are the application of unscented Kalman filters, improvement of moving horizon estimation via computation of the arrival cost and different implementations of extended Kalman filters. First the use of several estimation algorithms such as linearized Kalman filter (LKF), extended Kalman filter (EKF), unscented Kalman filter (UKF) and moving horizon estimation (MHE) are investigated for nonlinear systems with special emphasis on UKF as it is a relatively new technique. Detailed case studies show that UKF has advantages over EKF for highly nonlinear unconstrained estimation problems while MHE performs better for systems with constraints. Moving horizon estimation alleviates the computational burden of solving a full information estimation problem by considering a finite horizon of the measurement data; however, it is non-trivial to determine the arrival cost. A commonly used approach for computing the arrival cost is to use a first order Taylor series approximation of the nonlinear model and then apply an extended Kalman filter. The second contribution of this dissertation is that an approach to compute the arrival cost for moving horizon estimation based on an unscented Kalman filter is proposed. It is found that such a moving horizon estimator performs better in some cases than if one based on an extended Kalman filter. It is a promising alternative for approximating the arrival cost for MHE. Many comparative studies, often based upon simulation results, between extended Kalman filters (EKF) and other estimation methodologies such as moving horizon estimation, unscented Kalman filter, or particle filtering have been published over the last few years. However, the results returned by the extended Kalman filter are affected by the algorithm used for its implementation and some implementations of EKF may lead to inaccurate results. In order to address this point, this dissertation investigates several different algorithms for implementing extended Kalman filters. Advantages and drawbacks of different EKF implementations are discussed in detail and illustrated in some comparative simulation studies. Continuously predicting covariance matrix for EKF results in an accurate implementation. Evaluating covariance matrix at discrete times can also be applied. Good performance can be expected if covariance matrix is obtained from integrating the continuous-time equation or if the sensitivity equation is used for computing the Jacobian matrix.