Browsing by Subject "Kalman Filter"
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Item Improvements to a queue and delay estimation algorithm utilized in video imaging vehicle detection systems(Texas A&M University, 2007-09-17) Cheek, Marshall TylerVideo Imaging Vehicle Detection Systems (VIVDS) are steadily becoming the dominant method for the detection of vehicles at a signalized traffic approach. This research is intended to investigate the improvement of a queue and delay estimation algorithm (QDA), specifically the queue detection of vehicles during the red phase of a signal cycle. A previous version of the QDA used a weighted average technique that weighted previous estimates of queue length along with current measurements of queue length to produce a current estimate of queue length. The implementation of this method required some effort to calibrate, and produced a bias that inherently estimated queue lengths lower than baseline (actual) queue lengths. It was the researcher??????s goal to produce a method of queue estimation during the red phase that minimized this bias, that required less calibration, yet produced an accurate estimate of queue length. This estimate of queue length was essential as many other calculations used by the QDA were dependent upon queue growth and length trends during red. The results of this research show that a linear regression method using previous queue measurements to establish a queue growth rate, plus the application of a Kalman Filter for minimizing error and controlling queue growth produced the most accurate queue estimates from the new methods attempted. This method was shown to outperform the weighted average technique used by the previous QDA during the calibration tests. During the validation tests, the linear regression technique was again shown to outperform the weighted average technique. This conclusion was supported by a statistical analysis of data and utilization of predicted vs. actual queue plots that produced desirable results supporting the accuracy of the linear regression method. A predicted vs. actual queue plot indicated that the linear regression method and Kalman Filter was capable of describing 85 percent of the variance in observed queue length data. The researcher would recommend the implementation of the linear regression method with a Kalman Filter, because this method requires little calibration, while also producing an adaptive queue estimation method that has proven to be accurate.Item Integrated Algorithms and Multiple Antenna Techniques for Direction of Arrival (DOA) Estimation(2013-04-25) Xia, ZhenchunIn this dissertation, we design and develop a novel direction-of-arrival (DOA) finding system. We investigate the problems of DOA finding using canonical and crystallographic antenna array structures, develop a novel integrated algorithm consisting of combined multiple signal classification (MUSIC) algorithm, Kalman Filter and Kent Distribution to improve the accuracy and robustness of DOA estimation, design and conduct the real time testing of DOA and verify the accuracy and efficiency of the designed DOA finding system. We first examine the ability of mitigating the aliasing and enhancing the DOA estimation of different antenna structures, including canonical and crystallographic antenna structures. Our results show that the crystallographic antenna array has a better performance of overcoming aliasing in many circumstances, improving the estimation accuracy and covering more spatial region of DOA estimation. Then we propose a novel integrated algorithm to achieve a more robust DOA finding with higher accuracy. We show that the DOA estimation using MUSIC algorithm can be strongly influenced by the size, spacing and distributions of elements of the receiving antenna array as well as noise and mutual coupling. We propose a combined MUSIC and Kalman Filter algorithm to reduce the noise and enhance the robustness of the DOA estimation. Further more we map the DOA estimation onto the sphere and use Kent distribution to characterize the spread of DOA points on the sphere. We calculate the mean direction of Kent distribution to present the DOA vector, which further improves the accuracy of DOA finding. At last, we design and build a multi-channel and real time automated measurement system to validate the proposed antenna structure and integrated algorithms. Our testing results indicate that the designed DOA finding system can work practically and efficiently, with higher accuracy and stronger robustness.Item Navigation solution for the Texas A&M autonomous ground vehicle(Texas A&M University, 2006-10-30) Odom, Craig AllenThe need addressed in this thesis is to provide an Autonomous Ground Vehicle (AGV) with accurate information regarding its position, velocity, and orientation. The system chosen to meet these needs incorporates (1) a differential Global Positioning System, (2) an Inertial Measurement Unit consisting of accelerometers and angular-rate sensors, and (3) a Kalman Filter (KF) to fuse the sensor data. The obstacle avoidance software requires position and orientation to build a global map of obstacles based on the returns of a scanning laser rangefinder. The path control software requires position and velocity. The development of the KF is the major contribution of this thesis. This technology can either be purchased or developed, and, for educational and financial reasons, it was decided to develop instead of purchasing the KF software. This thesis analyzes three different cases of navigation: one-dimensional, two dimensional and three-dimensional (general). Each becomes more complex, and separating them allows a three step progression to reach the general motion solution. Three tests were conducted at the Texas A&M University Riverside campus that demonstrated the accuracy of the solution. Starting from a designated origin, the AGV traveled along the runway and then returned to the same origin within 11 cm along the North axis, 19 cm along the East axis and 8 cm along the Down axis. Also, the vehicle traveled along runway 35R which runs North-South within 0.1????, with the yaw solution consistently within 1???? of North or South. The final test was mapping a box onto the origin of the global map, which requires accurate linear and angular position estimates and a correct mapping transformation.Item Online parameter estimation applied to mixed conduction/radiation(Texas A&M University, 2005-08-29) Shah, Tejas JagdishThe conventional method of thermal modeling of space payloads is expensive and cumbersome. Radiation plays an important part in the thermal modeling of space payloads because of the presence of vacuum and deep space viewing. This induces strong nonlinearities into the thermal modeling process. There is a need for extensive correlation between the model and test data. This thesis presents Online Parameter Estimation as an approach to automate the thermal modeling process. The extended Kalman fillter (EKF) is the most widely used parameter estimation algorithm for nonlinear models. The unscented Kalman filter (UKF) is a new and more accurate technique for parameter estimation. These parameter estimation techniques have been evaluated with respect to data from ground tests conducted on an experimental space payload.