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Asynchronous Data Fusion for AUV Navigation Using Extended Kalman Filtering
NAVAL POSTGRADUATE SCHOOL MONTEREY CA DEPT OF MECHANICAL ENGINEERING
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A truly Autonomous Vehicle must be able to determine its global position in the absence of external transmitting devices. This requires the optimal integration of all available organic vehicle attitude and velocity sensors. This thesis investigates the extended Kalman filtering method to merge asynchronous heading, heading rate, velocity, and DGPS information to produce a single state vector. Different complexities of Kalman filters, with biases and currents, are investigated with data from Florida Atlantics Ocean Explorer II surface run. This thesis used a simulated loss of DGPS data to represent the vehicles submergence. All levels of complexity of the Kalman filters are shown to be much more accurate then the basic dead reckoning solution commonly used aboard autonomous underwater vehicles.
APPROVED FOR PUBLIC RELEASE