Research on the information fusion method of the Global Positioning System-dead reckoning vehicle integrated navigation system

摘要

To overcome the disadvantages of the conventional federated Kalman filter, a fuzzy Kalman filter based on a genetic algorithm (GA) is presented in this paper and applied in the information fusion of the Global Positioning System-dead reckoning vehicle integrated navigation system. The noise covariance and information distribution coefficient of the local filtering are modified online by the fuzzy logic adaptive controller in order to make the Kalman filtering optimal and to improve the positioning accuracy of the integrated navigation system. The acquisition of the membership function of a fuzzy controller usually relies to a great extent on empirical and heuristic knowledge. In this paper, the GA is used to optimize the fuzzy membership function and obtain the optimal or suboptimal control effect. The results of simulations demonstrate the feasibility and effectiveness of the method.