Abstract:

For addressing poor inference precision with canonical particle filtering resulting from weight degeneracy and sample impoverish, a new particle filtering algorithm is proposed, which utilizes the improved particle swarm optimization for improving priori particles distribution. Through adaptively adjusting inertia weight, particles exploration ability and exploitation ability are both enhanced so that premature phenomenon with particle swarm optimization is weakened. As a result, particles can move toward high likelihood areas, which can effectively increase status inference precision. The proposed algorithm validity is measured by Crame′rRao lower bound. Simulation results show that the proposed particle filtering is valid and stable.