Particle Filter for Dynamic Bayesian Networks Inference Based on BK Algorithm
To the high dimension problem of traditional particle filter for Dynamic Bayesian Networks inference,based on BK algorithm,a novel particle filter inference algorithm(BKPF) is proposed.The BKPF algorithm combines PF's and BK's advantages,and the relative clusters of DBNs are created under the guidance of weakly interaction to reduce the dimension of problem solving.Particles are maintained over clusters of state variables,the belief state is represented as a product of particles.Ulteriorly,resampling and update over the entire state spaces of DBNs.Simulation results show that the proposed algorithm improves PF's computational efficiency notably and achieves more precise results.