Abstract
The traditional particle filter (PF) algorithm is well known for signal noise reduction processing, but it exists problems of particle impoverishment and cumulation of estimation errors. An optimized PF algorithm called RBF-PF is proposed in this paper, which uses radial basis function network for training and optimizing the process of particle filter in the sampling. Experimental analysis verifies that the new method used to gain the signal-to-noise ratio is better than traditional PF algorithm during dealing with the added noise signal.
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