Abstract
Kalman filter is used commonly in agriculture vehicle navigation, but it is limited in linear system and has special request on noises. In practice, it is difficult to meet all the requests. To avoid the disadvantage of kalman filter, the RBF neural network is used to fuse the multi-sensor information to get the position information; Particle Swarm Optimization theory is introduced to the learning RBF neural network training process. The best neural network structure can be found by using particle swarm optimization algorithm to optimize the parameters of the RBF neural network. Experiment results indicate that RBF algorithm is better than the kalman filter, which can obtain more precise and more robust position information.
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