Particle Filter with Novel Resampling Algorithm: A Diversity Enhanced Particle Filter

AbstractIn this paper a particle filter (PF) with novel resampling algorithm called diversity enhanced-particle filter (DE-PF) is proposed. The major problem in using existing PF for non linear parameter estimation is particle impoverishment due to its present sequential importance resampling process. To solve this problem, our DE-PF uses a novel resampling algorithm based on combination process to obtain a new set of resampled particles contain more state information of their adjacent particles also. Hence, the output particles can express the posterior PDF of the state better. Also, simulations indicate that the proposed DE-PF can evidently improve estimation accuracy.

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