Abstract: By bringing the thought of swarm intelligence into particle filtering, a novel particle filter based on the artificial fish school algorithm is proposed. This algorithm makes prior particles move towards the high likelihood region by use of the alternation of behaviors of preying and swarming in the artificial fish school algorithm. So particle distribution and filtering accuracy are improved. Moreover, the difference between the particle distribution produced by behavior of swarming and the likelihood distribution is described by Kullback information. Kullback information decreases with the increasing iteration degree, which proves that this algorithm is rational. Finally, simulation results show that this swarm intelligence algorithm for particle filtering is effective, and has a better filtering performance than the EKF and the common PF.

Key words: particle filtering, swarm intelligence, artificial fish school algorithm, Kullback information