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.

Parametric uncertainty is one of many possible causes of divergence for the Kalman filter. Frequently, state estimation errors caused by imperfect model parameters are reduced by including the uncertain parameters as states (i.e., augmenting the state vector). For many situations, this not only improves the state estimates, but also improves the accuracy and precision of the parameters themselves. Unfortunately, not all filters benefit from this augmentation due to computational restrictions or because the parameters are poorly observable. A parameter with low observability (e.g., a set of high order gravity coefficients, a set of camera offsets, lens calibration controls, etc.) may not acquire enough measurements along a particular trajectory to improve the parameter's accuracy, which can cause detrimental effects in the performance of the augmented filter. The problem is then how to reduce the dimension of the augmented state vector while minimizing information loss. This dissertation explored possible implementations of reduced-order filters which decrease computational loads while also minimizing state estimation errors. A theoretically rigorous approach using the ?consider" methodology was taken at discrete time intervals were explored for linear systems. The continuous dynamics, discretely measured (continuous-discrete) model was also expanded for use with nonlinear systems. Additional techniques for reduced-order filtering are presented including the use of additive process noise, an alternative consider derivation, and the minimum variance reduced-order filter. Multiple simulation examples are provided to help explain critical concepts. Finally, two hardware applications are also included to show the validity of the theory for real world applications. It was shown that the minimum variance consider Kalman filter (MVCKF) is the best reduced-order filter to date both theoretically and through hardware and software applications. The consider method of estimation provides a compromise between ignoring parameter error and completely accounting for it in a probabilistic sense. Based on multiple measures of optimality, the consider filtering framework can be used to account for parameter error without directly estimating any or all of the parameters. Furthermore, by accounting for the parameter error, the consider approach provides a rigorous path to improve state estimation through the reduction of both state estimation error and with a consistent variance estimate. While using the augmented state vector to estimate both states and parameters may further improve those estimates, the consider estimation framework is an attractive alternative for complex and computationally intensive systems, and provides a well justified path for parameter order reduction.

This thesis presents mobile positioning and tracking with non-line of sight (NLOS) mitigation using time difference of arrival (TDOA) in biased extended Kalman filter (BEKF) in indoor dense multipath Ultra-Wideband (UWB) environment. The most serious issues which render to influence accuracy for the time-based location system is NLOS problem. Kalman filters (KFs) are used for smoothing range measurement data, and a method with sliding window is proposed to process range data for calculating standard deviation in a hypothesis testing and then identifying NLOS scenarios. When the measured arrival time has been converted to range difference, the biased extended Kalman filter is proposed to mitigate the NLOS error in the certain base stations (BSs) for mobile station (MS) positioning and trajectory tracking. From the simulation results in the indoor positioning environment with measurement and NLOS error, the sliding window algorithm and biased extended Kalman filter have higher accuracy than other related methods for NLOS identification and mitigation in positioning.

The causal system is more practical then the noncausal system in the world. Causality implies only the past input can effect the future output. As a consequence, noncausal system is seldom investigation. The purpose of this thesis is to study the signal recury for a noncausal system. The principle of signal estimation is based upon the Wiener-Hopf equation. Therefore, the correlation computation is very important. By transforming the noncausal dynamic equations to a causal equation, we achieve a partial recursive computation structure for correlation computation. However the current input is not independent of the past signal in the noncausal system. Hence, the Mason Rule is applied to solved this problem to make the above recursive structure complete. Furthermore, a recursive computation of Mason Rule for stage propagation is developed in this thesis to accelerating the processing speed. Our algorithm is applied to image restoration. We first segment the image to find the required generating input ponen for each correlated region. Secondly, we extend our 1-D algorithms to 2-D algorithm to restore the image. Our method is compared with the method developed base upon the Gaussian Markov model. The experiments results demonstrate the advantage of method in both visual quailty and numerical results.

This dissertation presents an experimental and analytical study of the Vision Based Navigation system (VisNav). VisNav is a novel intelligent optical sensor system invented by Texas A&M University recently for autonomous proximity operations. This dissertation is focused on system calibration techniques and navigation algorithms. This dissertation is composed of four parts. First, the fundamental hardware and software design configuration of the VisNav system is introduced. Second, system calibration techniques are discussed that should enable an accurate VisNav system application, as well as characterization of errors. Third, a new six degree-of-freedom navigation algorithm based on the Gaussian Least Squares Differential Correction is presented that provides a geometrical best position and attitude estimates through batch iterations. Finally, a dynamic state estimation algorithm utilizing the Extended Kalman Filter (EKF) is developed that recursively estimates position, attitude, linear velocities, and angular rates. Moreover, an approach for integration of VisNav measurements with those made by an Inertial Measuring Unit (IMU) is derived. This novel VisNav/IMU integration technique is shown to significantly improve the navigation accuracy and guarantee the robustness of the navigation system in the event of occasional dropout of VisNav data.

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.

<p>This thesis presents a new method for iterative road grade estimation based on sensors that are commonplace in modern heavy duty vehicles. Estimates from multiple passes of the same road segment are merged together to form a road grade map, that is improved each time the vehicle revisits an already traveled route. The estimation algorithm is discussed in detail together with its implementation and experimental evaluation on real vehicles.</p><p> </p><p>An increasing need for goods and passenger transportation drives continuing worldwide growth in road transportation while environmental concerns, traffic safety issues, and cost efficiency are becoming more important. Advancements in microelectronics open the possibility to address these issues through new advanced driver assistance systems. Applications such as predictive cruise control, automated gearbox control, predictive front lighting control and hybrid vehicle state-of-charge control benefit from preview road grade information. Using global navigation satellite systems an exact vehicle position can be obtained. This enables stored maps to be used as a source of preview road grade information. The task of creating such maps is addressed herein by the proposal of a method where the vehicle itself estimates the road grade each time it travels along a road and stores the information for later use.</p><p> </p><p>The presented road grade estimation method uses data from sensors that are standard equipment in heavy duty vehicles equipped with map-based advanced driver assistance systems. Measurements of the vehicle speed and the engine torque are combined with observations of the road altitude from a GPS receiver in a Kalman filter, to form a road grade estimate based on a system model. The noise covariance parameters of the filter are adjusted during gear shifts, braking and poor satellite coverage. The estimated error covariance of the road grade estimate is then used together with its absolute position to update a stored road grade map, which is based on all previous times the vehicle has passed the same location.</p><p> </p><p>Highway driving trials detailed in the thesis demonstrate that the proposed method is capable of accurately estimating the road grade based on few road traversals. The performance of the estimator under conditions such as braking, gear shifting, and loss of satellite coverage is presented. The experimental results indicate that road grade estimates from the proposed method are accurate enough to be used in predictive vehicle control systems to enhance safety, efficiency, and driver comfort of heavy duty vehicles.</p>

<p>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. </p><p>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. </p><p>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. </p><p>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.</p>