With the advent of the Global Position System (GPS), we now have the ability to determine absolute position anywhere on the globe. Although GPS systems work well in open environments with no overhead obstructions, they are subject to large unavoidable errors when the reception from some of the satellites is blocked. This occurs frequently in urban environments, such as downtown New York City. GPS systems require at least four satellites visible to maintain a good position 'fix' . Tall buildings and tunnels often block several, if not all, of the satellites. Additionally, due to Selective Availability (SA), where small amounts of error are intentionally introduced, GPS errors can typically range up to 100 ft or more. This thesis proposes several methods for improving the position estimation capabilities of a system by incorporating other sensor and data technologies, including Kalman filtered inertial navigation systems, rule-based and fuzzy-based sensor fusion techniques, and a unique map-matching algorithm. / Master of Science

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The Kalman filter is an important component of optimal estimation theory. It has applications in a wide range of high performance control systems including navigational, fire control, and targeting systems. The Kalman filter, however, has not been utilized to its full potential due to the limitations of its inherent computational intensiveness which requires "off-line" processing or allows only low bandwidth real-time applications. The recent advances in VLSI circuit technology have created the opportunity to design algorithms and data structures for direct implementation in integrated circuits. A systolic architecture is a concept which allows the construction of massively parallel systems in integrated circuits and has been utilized as a means of achieving high data rates. A systolic system consists of a set of interconnected processing elements, each capable of performing some simple operation. The design of a processing element in an orthogonal systolic architecture will be investigated using the state of the art in VLSI technology. The goal is to create a high speed, high precision processing element which is adaptive to a highly configurable systolic architecture. In order to achieve the necessary high computational throughput, the arithmetic unit of the processing element will be implemented using the Logarithmic Number System. The Systolic architecture approach will be used in an attempt to implement a Kalman filtering system with both a high sampling rate and a small package size. The design of such a Kalman filter would enable this filtering technology to be applied to the areas of process control, computer vision, and robotics.

In the past, passive SONAR range tracking systems have used Extended Kalman filters to process nonlinear time-delay measurements. This approach has several flaws due to the inherent divergence problems of Extended Kalman filters. This paper discusses a new approach which uses a prefilter to linearize the measurements so that they can be processed by a standard Kalman filter. The approach is subsequently expanded for use with an adaptive Kalman filter which allows source maneuvers to be tracked. A new approach to passive Doppler velocity tracking is also proposed which uses a dedicated Kalman filter to track random fluctuations in the sources center frequency. This dedicated tracker simplifies the problem so that it can be handled by a basic adaptive Kalman filter. / Master of Science

The method of Pseudo-Linear Identification (PLID) is developed for application in an adaptive control loop. The effects of noise are investigated for the case of full-order system identification, and the results are applied to the use of PLID as a reduced-order system estimator. A self-tuning regulator (STR) is constructed using PLID and the effects of reducing the expected order of the system are demonstrated. A second adaptive control algorithm is presented wherein the STR controller is varied to achieve some degree of closeness to a given model (model-reference adaptive control). / Master of Science

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Approved for public release; distribution is unlimited / LosCon, the software program developed for the author's thesis and tested at sea, is designed to help the ASW commander regain tactical control in a loss of submarine contact situation. Persistent detection and cueing in the battlespace depend on utilizing contact reports from a network of combatant platform and offboard sensors. LosCon, an extended Kalman filter-based program modeled after MTST (Maneuvering Target Statistical Tracker), can integrate the sensor network very efficiently. Kalman filtering is a method of recursively updating the position of an evading target and accuracy of that position using imperfect measurements. Lines of bearing to the contact with associated standard deviation bearing errors and positions with their standard deviation range errors are the measurements LosCon uses to generate an ellipse of the submarine's likely position or AOU (Area Of Uncertainty). LosCon will also generate an expanded AOU for any future time, allowing commanders to correctly estimate the size of the search area. The effectiveness of the sea shield concept depends on the ability of organic forces to deny the enemy tactical control of the battlespace area. Incorporating the information generated by LosCon would assist ASW commanders in maintaining undersea superiority. / Ensign, United States Navy

Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1976 / Bibliography: leaves 145-148. / by Stuart C. Wright. / M.S. / M.S. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science