In solving target tracking problems, the Kalman filter (KF) is a systematic estimation algorithm. Whether the state of a moving target adapts to the changes in the observations depends on the model assumptions. The interacting multiple model (IMM) algorithm uses interaction of a bank of parallel KFs by updating associated model probabilities. Every parallel KF has its model probability adjusted by the dynamic system. For moving targets of different dynamic linear models, an IMM with two KFs generally performs well. In this thesis, in order to improve the performance of target tracking and state estimation, multi-sensor data fusion technique will be used. Same types of IMMs can be incorporated in the cooperative IMM-based algorithm. The IMM-based estimators exchange with each other the estimates, model robabilities and model transition probabilities. A distributed algorithm for multi-sensor tracking usually needs a fusion center that integrates decisions or estimates, but the proposed cooperative IMM-based algorithm does not use the architecture. Cooperative IMM estimator structures exchange weights and estimates on the platforms to avoid accumulation of errors. Performance of data fusion may degrade due to different kinds of undesirable environmental effects. The simulations show that an IMM estimator with smaller measurement noise level can be used to compensate the other IMM, which is affected by larger measurement noise. In addition, failure of a sensor will cause the problem that model probabilities can not be updated in the corresponding estimator. Kalman filters will not be able to perform state correction for the moving target. To tackle the problem, we can use the estimates from other IMM estimators by adjusting the corresponding weights and model probabilities. The simulations show that the proposed cooperative IMM structure effectively improve the tracking performance.

The thesis is to explore wireless location tracking algorithms based on geometric dilution of precision (GDOP) in the mobile environment. The GDOP can be used as an indication of positioning accuracy, affected by the geometric relationship between the target and sensing units. The smaller the GDOP is, the better positioning accuracy. By using the information of sensing units and time difference of arrival (TDOA) positioning method, we use extended Kalman filter as an estimator to track and predict the state of a moving target. From previous research, the lowest GDOP value, located at the center of a regular polygon, represents the best positioning accuracy in 2-D scenario with numerous sensing units. It is important to find the best locations for the sensing units. Simulated annealing algorithm was used in previous studies. However, it only finds a location at a time, and consumes computation load and time. Due to the above-mentioned reasons, we propose a location tracking system, which consists of a base traiver station and numerous mobile sensing units. By using the information of a base transceiver station and the predicted position of target, we can obtain the best locations for all the mobile sensing units with the calculation of rotation matrix. The locations can also be used as beacons for relocating mobile sensing units. It may take many cycles to move mobile sensing units to the best locations. We have to perform path planning for mobile sensing units. Due to the location change of the moving target, the routes need adjustment accordingly. If the predicted stay of a mobile sensing unit is inside the obstacle, we adjust the route of the mobile sensing unit to make it stay out of the obstacle. Therefore, we also propose a path planning scheme for mobile sensing units to avoid obstacles. Through simulations, the proposed method decreases the tracking time effectively, and find the best locations precisely. When mobile sensing units move toward the best locations, they successfully avoid obstacles and move toward the position with the minimum GDOP. Through the course, good positioning accuracy can be maintained.

This thesis presents an investigation of ensemble Gaussianity, the effect of non- Gaussianity on covariance structures, storm-centered data assimilation techniques, and the relationship between commonly used data assimilation variables and the underlying dynamics for the case of Hurricane Humberto. Using an Ensemble Kalman Filter (EnKF), a comparison of data assimilation results in Storm-centered and Eulerian coordinate systems is made. In addition, the extent of the non-Gaussianity of the model ensemble is investigated and quantified. The effect of this non-Gaussianity on covariance structures, which play an integral role in the EnKF data assimilation scheme, is then explored. Finally, the correlation structures calculated from a Weather Research Forecast (WRF) ensemble forecast of several state variables are investigated in order to better understand the dynamics of this rapidly intensifying cyclone. Hurricane Humberto rapidly intensified in the northwestern Gulf of Mexico from a tropical disturbance to a strong category one hurricane with 90 mph winds in 24 hours. Numerical models did not capture the intensification of Humberto well. This could be due in large part to initial condition error, which can be addressed by data assimilation schemes. Because the EnKF scheme is a linear theory developed on the assumption of the normality of the ensemble distribution, non-Gaussianity in the ensemble distribution used could affect the EnKF update. It is shown that multiple state variables do indeed show significant non-Gaussianity through an inspection of statistical moments. In addition, storm-centered data assimilation schemes present an alternative to traditional Eulerian schemes by emphasizing the centrality of the cyclone to the assimilation window. This allows for an update that is most effective in the vicinity of the storm center, which is of most concern in mesoscale events such as Humberto. Finally, the effect of non-Gaussian distributions on covariance structures is examined through data transformations of normal distributions. Various standard transformations of two Gaussian distributions are made. Skewness, kurtosis, and correlation between the two distributions are taken before and after the transformations. It can be seen that there is a relationship between a change in skewness and kurtosis and the correlation between the distributions. These effects are then taken into consideration as the dynamics contributing to the rapid intensification of Humberto are explored through correlation structures.

There has always been a desire to apply technology to human endeavors to increase a person's capabilities and reduce the numbers or skill level required of the people involved, or replace the people altogether. Three fundamental areas are investigated where technology can enable the reduction or removal of humans in complex tasks. The fi rst area of research is the rapid calibration of multiple camera systems when cameras share an overlapping fi eld of view allowing for 3D computer vision applications. A simple method for the rapid calibration of such systems is introduced. The second area of research is the autonomous exploration of hallways or other urbancanyon environments in the absence of a global positions system (GPS) using only an inertial motion unit (IMU) and a monocular camera. Desired paths that generate accurate vehicle state estimates for simple ground vehicles are identi fied and the bene fits of integrated estimation and control are investigated. It is demonstrated that considering estimation accuracy is essential to produce efficient guidance and control. The Schmidt-Kalman filter is applied to the vision-aided inertial navigation system in a novel manner, reducing the state vector size signi ficantly. The final area of research is a decentralized swarm based approach to source localization using a high fidelity environment model to directly provide vehicle updates. The approach is an extension of a standard quadratic model that provides linear updates. The new approach leverages information from the higher-order terms of the environment model showing dramatic improvement over the standard method.

Ultra Wideband (UWB) signal can offer an accurate location service in wireless sensor networks because its high range resolution. Target tracking by multiple sensors can provide better performance, but the centralized algorithms are not suitable for wireless sensor networks. In additional, the non line of sight (NLOS) propagation error leads to severe degradation of the accuracy in location systems. In this thesis, NLOS identification and mitigation technique utilizing modified biased Kalman filter (KF) is proposed to reduce the NLOS time of arrival (TOA) errors in UWB environments. We combine the modified biased Kalman filter with sliding window to identify and mitigate different degree of NLOS errors immediately. In order to deal with the influence of inaccurate NLOS angle of arrival (AOA) measurements, we also had a discussion on AOA selection and fusion methods. In the distributed location structure, we used the extended Information filter (EIF) to process the formulated time difference of arrival (TDOA) and AOA measurements for the target positioning and tracking. Instead of using extended Kalman filter, extended Information filter can assimilate selected AOA easily without dynamic dimensions. The sensors are divided into different groups for distributed TDOA/AOA location to reduce computation and then each group can assimilate information from other groups easily to maintain precise location. The simulation results show that the proposed architecture can mitigate NLOS errors effectively and improve the accuracy of target positioning and tracking from distributed location and data fusion in wireless sensor networks.

Interactions between macroeconomic fundamentals and term structure of interest rates be stronger according to the way of changes in structure of worldwide economy. Combined macro-finance analysis determines the joint dynamics of term structure of interest rates and macroeconomic fundamentals. This thesis provides analysis of two existing macro-finance models and an original one. Parameter estimations for these three macro-finance term structure models are done for monthly Turkish data by use of an efficient recursive estimator Kalman filter. In spite of the small scale application the results are satisfactory except first model but with longer sets of macroeconomic variables and interest rate data models provide more encouraging results.

In this thesis, the integration of a MEMS based inertial measurement unit and a three axis solid state magnetometer are studied. It is a fact that unaided inertial navigation systems, especially low cost MEMS based navigation systems have a divergent behavior. Nowadays, many navigation systems use GPS aiding to improve the performance, but GPS may not be applicable in some cases. Also, GPS provides the position and velocity reference whereas the attitude information is extracted through estimation filters. An alternative reference source is a three axis magnetometer, which provides direct attitude measurements. In this study, error propagation equations of an inertial navigation system are derived / measurement equations of magnetometer for Kalman filtering are developed / the unique method to self align the MEMS navigation system is developed. In the motion estimation, the performance of the developed algorithms are compared using a GPS aided system and magnetometer aided system. Some experiments are conducted for self alignment algorithms.

This study investigates the time varying behavior of the betas (systematic risk) for the Turkish REIT sector in an attempt to identify whether the betas for the Turkish REITs are stable and if not whether the declining trend valid for the REIT betas of many developed and developing countries is also observed for the Turkish REITs. Three different techniques / namely, Diagonal BEKK (DBEKK) GARCH model, the Schwert and Seguin model and the Kalman Filter algorithm, are employed in order to estimate and analyze the time varying betas of the Turkish REIT sector over the period 2002-2009. The empirical results suggest that, similar to many other countries, betas are not stable in the Turkish REIT sector. The general view of a declining beta trend for the REITs appears to prevail for Turkish REITs as well, reinforcing the defensive characteristics of these publicly traded real estate companies. Comparing the relative forecast accuracy of the three techniques employed, Schwert and Seguin model performs the worst both for weekly and daily data / whereas the Kalman Filter and the DBEKK Garch models provide the lowest forecast errors for the weekly and the daily data, respectively. This study also shows that the use of the data sets with different frequency could lead to different empirical findings.

An Inertial Navigation System (INS) can individually produce the navigation data, i.e.position, velocity, of the aircraft without any help or aid. However, a large number of errors are ntroduced by sensors causing to an unacceptable drift in the output. Because of this reason, external aids are used to correct INS. Using these aids an integrated navigation structure is developed. In an integrated navigation system, INS output is used to alculate current navigation states / aid is used to supply external measurements and dierent algorithms are used to provide the most probable corrections to the state estimate using all data. One of the integrated navigation approaches is Terrain Aided Navigation (TAN). Terrain Aided Navigation is a technique to estimate the position of a moving vehicle by comparing the measured terrain profile under the vehicle to a stored map, DTED. This thesis describes the theoretical aspects implementation of a simulation environment, simulations of the implemented Kalman Filtering TAN algorithms with developed INS model. In order to perform the study, first a thorough survey of the literature on TAN navigation algorithms is performed. Then, we have developed a dynamics simulation environment. A flight profile generator is designed. Since, the main issue of this work is to correct INS, an Strapdown INS model developed using Matlab INS Toolbox. Therefore, to model a Strapdown INS, mathematical equations of INS system are derived and they are linearized to form linear error model. In addition, a radar altimeter simulator is also developed that provides measurement to the error dynamics. Then, a Kalman filter structure is designed and implemented using Matlab. The simulations are done with dierent linearization approaches using Kalman filter structure. Finally, the performance of the implemented algorithms are evaluated.

This study describes the design and implementation of the altitude and heading autopilot algorithms for a fixed wing unmanned air vehicle and navigation algorithm for attitude and heading reference outputs. Algorithm development is based on the nonlinear mathematical model of Middle East Technical University Tactical Unmanned Air Vehicle (METU TUAV), which is linearized at a selected trim condition. A comparison of nonlinear and linear mathematical models is also done. Based on the linear mathematical model of the METU TUAV, the classical control methods are applied during the design process of autopilot algorithms. For the confirmation purposes of the autopilot and navigation algorithms, a nonlinear simulation environment is developed in Matlab/Simulink including nonlinear model of the METU TUAV, altitude and heading autopilot loops, nonlinear actuator models, sensor models and navigation model. In the first part of the thesis, feedback signals for the controller are provided by IMU free measurements. In the second part, the feedback signals are provided by an attitude and heading reference mode, which incorporates the gyroscope solutions with the magnetic sensor and accelerometer sensor measurements by using a Kalman filter algorithm. The performance comparison of the controller is done for both cases where the effects of having different modes of the measurement sources are investigated.