Discrete asynchronous Kalman filtering of navigation data for the Phoenix autonomous underwater vehicle
McClarin, David W.
McGhee, Robert B.
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The Phoenix Autonomous Underwater Vehicle must be able to accurately determine its position at all times. This requires: (1) GPS and differential GPS for surface navigation, (2) short baseline sonar ranging system for submerged navigation, and (3) mathematical modeling of position. This thesis describes a method of Kalman filtering to merge the GPS, differential GPS, short baseline sonar ranging, and the mathematical model to produce a single state vector of vehicle position and ocean currents. The filter operates in the extended mode for processing the nonlinear sonar ranges, and in normal mode for the linear GPS/DGPS data. This required installation of a GPS system and the determination of the different variances and errors between these systems. Phoenix now has a real time method of position determination using either position measuring system separately or combined. The results of this work have been validated by real world testing of the vehicle at sea, where position estimates accurate to within several meters were obtained.
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