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

Abstract

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.

Keywords

autonomous underwater vehicles navigation sensor fusion Kalman filters extended Kalman filters fuzzy logic multiobjective genetic algorithm

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