动态贝叶斯网络的一种基于BK的粒子滤波推理算法

Particle Filter for Dynamic Bayesian Networks Inference Based on BK Algorithm

针对传统粒子滤波(PF)对于动态贝叶斯网络推理中计算的高维问题,提出动态贝叶斯网络的一种基于BK的粒子滤波推理算法(BKPF).BKPF算法融合PF和BK的优点,以弱相关性为指导对DBNs进行分团来降低问题求解的规模,从每个团的状态空间获取粒子并以粒子的因式积形式近似表示系统的状态信度,进而对DBNs的状态空间进行重采样和更新.仿真实验表明,与PF相比,该算法显著提高了计算效率,且推理精度也有一定的提高.

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.