A fuzzy Kalman filter optimized using a multi-objective genetic algorithm for enhanced autonomous underwater vehicle navigation

In an autonomous underwater vehicle integrated navigation system, short-term temporal accuracy is provided by an inertial navigation subsystem (INS) and long-term accuracy by a global positioning system (GPS). The Kalman filter has been a popular method for integrating the data produced by the two systems to provide optimal estimates of autonomous underwater vehicle position and attitude. In this paper, a sequential use of a linear Kalman filter and extended Kalman filter is proposed. The former is used to fuse the data from a variety of INS sensors whose output is used as an input to the latter where integration with GPS data takes place. The use of a fuzzy-rule-based adaptation scheme to cope with the divergence problem caused by the insufficiently known a priori filter statistics is also explored. The choice of fuzzy membership functions for an adaptation scheme is first carried out using a heuristic approach. Multiobjective genetic algorithm techniques are then used to optimize the parameters of the membership functions with respect to a certain performance criteria in order to improve the overall accuracy of the integrated navigation system. Simulation results are presented that show that the proposed algorithms can provide a significant improvement in the overall navigation performance of an autonomous underwater vehicle navigation system.

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