An intelligent navigation system, based on the integrated use of the GPS (Global Positioning System) and several INS (Inertial Navigation System) sensors, was implemented for AUV (Autonomous Underwater Vehicle) applications. A SKF (Simple Kalman Filter) and an EKF (Extended Kalman Filter) are proposed to be used subsequently to fuse the data from the INS sensors and to integrate them with the GPS data. Fuzzy logic techniques are used to the adaptation of the initial statistical assumption of both the SKF and EKF caused by possible changes in sensor noise characteristics. This adaptive mechanism is considered to be necessary as the SKF and EKF can only maintain their stability and performance when the algorithms contain the true sensor noise characteristics. In addition, fault detection and signal recovery algorithms during the fusion process to enhance the reliability of the navigation systems are also discussed herein. The proposed algorithms are implemented to real experimental data obtained from a series of AUV trials conducted by running the low-cost Hammerhead AUV, developed by the University of Plymouth and Cranfield University.
Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system
Adaptive Einstellung eines Kalman-Filters mit Hilfe von Fuzzy-Logik für die Navigation eines automatischen Unterwasserfahrzeuges
Control Engineering Practice ; 12 , 12 ; 1531-1539
2004
9 Seiten, 13 Bilder, 2 Tabellen, 23 Quellen
Aufsatz (Zeitschrift)
Englisch
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