Research on GRNN-based Particle Filter Algorithm

Aiming at the weight degeneracy phenomena in fundamental particle filter algorithm, the particle filtering algorithm improving the important samples based on generalized regression neural network (GRNN) was presented. This algorithm optimizes the samples from importance density function by GRNN. The samples of particle filter are selected as the neural network input and the observed values as the neural network target vector. The smooth factors are optimized to approach the target vector through multiple trainings, and the samples and the corresponding surrounding adjusted values are regarded as the input of the neural network after training. The best optimized values are used to replace the sample indicated by the output vector indicator of neural network. The samples are adjusted by GRNN so as to be closer to the posterior probability density. The simulation results show that the performance of particle filtering algorithm based on generalized regression neural network is better than that of basic particle filtering algorithm in terms of effective particle number and mean square error parameter. Good results have been achieved in improving the filtering precision. It is proved that the generalized regression neural network is available and effective in particle filtering algorithm.