Adaptive algorithms for state estimation are currently of tremendous interest. Such estimation techniques have particular military usefulness in automatic gunfire control systems. The conventional Kalman filter, developed by Kalman and Bucy, optimally solves the state estimation problem concerning linear systems with Gaussian disturbance and error processes. The maneuvering target tracking problem generally involves nonlinear system properties as well as non-Gaussian disturbance processes. The study presented here explores several solutions. to this problem. An adaptive state estimator centered about the familiar Kalman filter has been developed for applications in three-dimensional maneuvering target tracking. Target maneuvers are modeled in a general manner by a semi-Markov process. The semi-Markov modeling is based on very intuitively appealing assumptions. Specifically, target maneuvers are randomly selected from a range (possibly infinite) of maneuver commands. The selected command is sustained for a random holding time before another command is selected. Dynamics of the selection and holding process may be stationary or time varying. By incorporating the semi-Markov modeling into a Baysian estimation scheme, an adaptive state estimator can be designed to identify the particular maneuver command influencing the target. The algorithm has the distinct advantages of requiring only one Kalman filter and non-growing computer storage requirements. Several techniques of implementing the adaptive algorithm have been developed. The merits of rectangular and spherical modeling have been explored. Most importantly, the planar discrete level semi-Markov algorithm, originally developed for sonar applications, has been extended to a continuum of levels, as well as extended to three-dimensional tracking. The developed algorithms have been fully evaluated by computer simulations. Emphasis has been placed on computational burden as well as overall tracking performance. Results are presented that show.that the developed estimators largely eliminate severe tracking errors that occur when more simplistic target models are incorporated. / Ph. D.

The simulation and testing of several state tracking techniques over a range of process and measurement noise environments is considered. The problem is placed in the context of tracking a maneuvering vehicle from noisy position data with the vehicle accelerations considered as a random process about which the first and second order statistics are known. The tracking filters under test are the fixed α-β filter, the double α-β filter, the second order Kalman filter, the augmented Kalman filter, and the double Kalman filter. All filters show improved performance as the measurement noise increases and the process noise decreases. The superiority of the Kalman filter over the simpler deterministic digital trackers decreases as the measurement noise increases and the process noise decreases. The double Kalman filter, with the capability of adaptive adjustments of threshold values, indicates the best overall tracking for combined maneuver and non-maneuver tracking. / Master of Science

Research on Multiple Object Tracking (MOT) has typically involved 2D displays where stimuli move in a single depth plane. However, under natural conditions, objects move in 3D which adds complexity to tracking. According to the spatial interference model, tracked objects have an inhibitory surround that when crossed causes tracking errors. How do these inhibitory fields translate to 3D space? Does multiple object tracking operate on a 2D planar projection, or is it in fact 3D? To investigate this, we used a fully immersive virtual-reality environment where participants were required to track 1 to 4 moving objects. We compared performance to a condition where participants viewed the same stimuli on a computer screen with monocular depth cues. Results suggest that participants were more accurate in the VR condition than the computer screen condition. This demonstrates interference is negligent when the objects are spatially distant, yet proximate within the 2D projection. / Includes bibliography. / Thesis (M.A.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Yu Sheung Fan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 96-99). / Abstracts in English and Chinese. / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Background: Context-Aware Applications --- p.2 / Chapter 1.1.1 --- Definitions of Context --- p.2 / Chapter 1.1.2 --- Existing Applications --- p.3 / Chapter 1.1.3 --- Review --- p.6 / Chapter 1.2 --- Research Motivation --- p.6 / Chapter 1.3 --- Research Contributions --- p.8 / Chapter 1.4 --- Thesis Outline --- p.8 / Chapter 2. --- Location-sensing Technologies --- p.9 / Chapter 2.1 --- Global Positioning System (GPS) --- p.9 / Chapter 2.2 --- Existing indoor Location-sensing Systems --- p.11 / Chapter 2.2.1 --- Active Badge --- p.11 / Chapter 2.2.2 --- The Bat System --- p.12 / Chapter 2.2.3 --- RADAR --- p.13 / Chapter 2.2.4 --- PinPoint 3D-iD --- p.14 / Chapter 2.2.5 --- Easy Living --- p.15 / Chapter 2.3 --- System Properties and Risks --- p.16 / Chapter 2.3.1 --- Accuracy --- p.17 / Chapter 2.3.2 --- Cost --- p.18 / Chapter 2.3.3 --- User Privacy --- p.18 / Chapter 2.3.4 --- Location Representation --- p.19 / Chapter 2.3.5 --- Other Limitations --- p.20 / Chapter 2.4 --- Design Goals --- p.20 / Chapter 2.4.1 --- Operate Inside Buildings --- p.21 / Chapter 2.4.2 --- Preserve User Privacy --- p.21 / Chapter 2.4.3 --- Low Cost --- p.22 / Chapter 2.4.4 --- Fast Response --- p.22 / Chapter 2.4.5 --- Spatial Recognition --- p.23 / Chapter 2.4.6 --- Easy Administration and Deployment --- p.23 / Chapter 2.5 --- Summary --- p.23 / Chapter 3. --- System Design --- p.25 / Chapter 3.1 --- System Architecture --- p.25 / Chapter 3.2 --- Position-sensing Platform --- p.28 / Chapter 3.2.1 --- Platform Architecture --- p.29 / Chapter 3.2.2 --- Transmission Format --- p.30 / Chapter 3.2.3 --- Distance Measurement --- p.31 / Chapter 3.2.4 --- Position Estimation --- p.32 / Chapter 3.2.5 --- Noise Cancellation --- p.35 / Chapter 3.2.6 --- Location Inference --- p.36 / Chapter 3.3 --- Summary --- p.38 / Chapter 4. --- System Implementation --- p.39 / Chapter 4.1 --- Communication Technologies --- p.39 / Chapter 4.1.1 --- Ultrasound --- p.40 / Chapter 4.1.2 --- Radio Frequency Identification (RFID) --- p.40 / Chapter 4.1.3 --- Infrared Data Association (IrDA) --- p.41 / Chapter 4.1.4 --- Bluetooth --- p.42 / Chapter 4.2 --- Technologies Overview --- p.43 / Chapter 4.2.1 --- Positioning --- p.44 / Chapter 4.2.2 --- Networking --- p.44 / Chapter 4.2.3 --- Communication Protocol --- p.45 / Chapter 4.2.4 --- Range --- p.45 / Chapter 4.2.5 --- Angle Dependency --- p.45 / Chapter 4.2.6 --- Hardware supports --- p.46 / Chapter 4.3 --- Hardware --- p.46 / Chapter 4.3.1 --- Mobile Receiver --- p.46 / Chapter 4.3.2 --- Transmitter --- p.47 / Chapter 4.4 --- Software --- p.47 / Chapter 4.4.1 --- Communication Protocol --- p.48 / Chapter 4.4.2 --- Programming Environment --- p.48 / Chapter 4.4.3 --- Signal Generation Routine --- p.48 / Chapter 4.4.4 --- Position Estimation Routine --- p.50 / Chapter 4.5 --- Summary --- p.53 / Chapter 5. --- Evaluation --- p.55 / Chapter 5.1 --- Platform Calibration --- p.55 / Chapter 5.1.1 --- Outliers Elimination --- p.56 / Chapter 5.1.2 --- Delay Determination --- p.58 / Chapter 5.1.3 --- Window Size Determination --- p.61 / Chapter 5.1.4 --- Revised Position Estimation Algorithm --- p.63 / Chapter 5.2 --- Platform Evaluation - IrDA Figure 5.9: Experimental setup for distance performance evaluation --- p.64 / Chapter 5.2.1 --- Distance Measurement Figure 5.10: IrDA horizontal distance measurement experiment results --- p.66 / Chapter 5.2.2 --- Position Estimation - Static --- p.66 / Chapter 5.2.3 --- Position Estimation - Mobile --- p.68 / Chapter 5.3 --- Platform Evaluation - Bluetooth --- p.69 / Chapter 5.3.1 --- Distance Measurement --- p.69 / Chapter 5.3.2 --- Position Estimation - Static --- p.70 / Chapter 5.3.3 --- Position Estimation ´ؤ Mobile --- p.71 / Chapter 5.4 --- Summary --- p.73 / Chapter 6. --- Applications --- p.74 / Chapter 6.1 --- Potential Applications --- p.74 / Chapter 6.1.1 --- Resource Tracking Systems --- p.75 / Chapter 6.1.2 --- Shopping Assistance System --- p.76 / Chapter 6.1.3 --- Doctor Tracking System --- p.77 / Chapter 6.1.4 --- Tourist Guide Application --- p.78 / Chapter 6.1.5 --- Other Applications --- p.79 / Chapter 6.2 --- System Limitations --- p.79 / Chapter 6.3 --- Summary --- p.79 / Chapter 7. --- Conclusion --- p.80 / Chapter 7.1 --- Summary --- p.80 / Chapter 7.2 --- Future Work --- p.81 / Chapter Appendix A: --- IrDA --- p.86 / Chapter A.1 --- IrDA Physical Layer --- p.86 / Chapter A.2 --- Physical Aspects of IrDA Physical Layer --- p.87 / Chapter A.3 --- Discovering Other IrDA Devices --- p.88 / Chapter A.4 --- Connection of IrDA Devices --- p.89 / Chapter Appendix B: --- Bluetooth --- p.91 / Chapter B.1 --- Bluetooth Stack --- p.91 / Chapter B.2 --- Radio --- p.92 / Chapter B.3 --- Frequency Hopping --- p.92 / Chapter B.4 --- Package Structure --- p.92 / Chapter B.5 --- The Link Controller --- p.93 / Chapter B.6 --- The Link Manager --- p.93 / Chapter B.7 --- Logical Link Control and Adaptation Protocol --- p.94 / Chapter B.8 --- The Service Discovery Protocol --- p.94 / Chapter B.9 --- Encryption and Security --- p.95 / Bibliography --- p.96

Abstract not available

Abstract not available

Bibliography: p. 231-232. / xvi, 232 p. : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / The aim of this thesis is detecting and tracking objects at large ranges, when no target features are visible, with imaging type sensors. A system which estimates the optical flow of the scene in a parallel architecture is developed. The architecture is similar to that of an artifical neural network. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1994

Indoor localization systems have a variety of applications such as tracking of assets, indoor robot navigation, and monitoring of people (e.g. patients) in hospitals or at home. Global positioning system (GPS) offers location accuracy of several meters and is mainly used for outdoor location-based applications as its accuracy degrades significantly in indoor scenarios. Wireless local area networks (WLAN) have also been used for indoor localization, but the accuracy is too low and power consumption of WLAN terminals is too high for most applications. Ultra-wideband (UWB) localization is superior in terms of accuracy and power consumption compared with GPS and WLAN localization, and is thus more suitable for most indoor location-based applications [1-4]. The accuracy and precision requirements of localization systems depend on the specific characteristics of the applications. For example, centimeter or even millimeter localization accuracy is required for dynamic part tracking, while decimeter accuracy might be sufficient for tracking patients in hospitals or at home. Note that accuracy is not the only aspect of the overall performance of the system. Factors such as cost, range, and complexity should also be considered in system design. In the first part of this dissertation, a centimeter-accurate UWB localization system is developed. The technical challenges to achieve centimeter localization accuracy are investigated. Since all the receivers are synchronized through wire connection in this system, a wireless localization system with centimeter accuracy is introduced in order to make the system easier for deployment. A two-step synchronization algorithm with picosecond accuracy is presented, and the system is tested in a laboratory environment. The second part of this dissertation focuses on reducing the complexity of UWB localization systems when the localization accuracy requirement is relaxed. An UWB three-dimensional localization scheme with a single cluster of receivers is proposed. This scheme employs the time-of-arrival (TOA) technique and requires no wireless synchronization among the receivers. A hardware and software prototype that works in the 3.1-5.1 GHz range is constructed and tested in a laboratory environment. An average position estimation error of less than 3 decimeter is achieved by the experimental system. This TOA scheme with receivers in a single unit requires synchronization between the transmitter and the receiver unit. In order to further reduce system complexity, a new time-difference-of-arrival localization scheme is proposed. This scheme requires multiple units, each operating on its own clock. It avoids synchronization between the transmitter and receivers, and thus makes the development of the transmitter extremely simple. The performance of this system is simulated and analyzed analytically, and turns out to be satisfactory for most indoor localization applications. / Graduation date: 2013

by Kenneth M. Keverian, Nils R. Sandell, Jr. / Office of Naval Research Contract ONR/N00014-77-C-0532 (85552). / Originally presented as the first author's thesis, (B.S.) in the M.I.T. Dept. of Electrical Engineering and Computer Science, 1979. / Bibliography: p. 114-115.

This thesis aims to contribute to the area of visual tracking, which is the process of identifying an object of interest through a sequence of successive images. The thesis explores kernel-based statistical methods, which map the data to a higher dimensional space. A pre-image framework is provided to find the mapping from the embedding space to the input space for several manifold learning and dimensional learning algorithms. Two algorithms are developed for visual tracking that are robust to noise and occlusions. In the first algorithm, a kernel PCA-based eigenspace representation is used. The de-noising and clustering capabilities of the kernel PCA procedure lead to a robust algorithm. This framework is extended to incorporate the background information in an energy based formulation, which is minimized using graph cut and to track multiple objects using a single learned model. In the second method, a robust density comparison framework is developed that is applied to visual tracking, where an object is tracked by minimizing the distance between a model distribution and given candidate distributions. The superior performance of kernel-based algorithms comes at a price of increased storage and computational requirements. A novel method is developed that takes advantage of the universal approximation capabilities of generalized radial basis function neural networks to reduce the computational and storage requirements for kernel-based methods.