We present a particle filter construction for a system that exhibits time-scale separation. The separation of time-scales allows two simplifications that we exploit: i) The use of the averaging principle for the dimensional reduction of the system needed to solve for each particle and ii) the factorization of the transition probability which allows the Rao-Blackwellization of the filtering step. Both simplifications can be implemented using the coarse projective integration framework. The resulting particle filter is faster and has smaller variance than the particle filter based on the original system. The convergence of the new particle filter to the analytical filter for the original system is proved and some numerical results are provided.
标签： construction separation time-scale particle
上传时间： 2016-01-02
上传用户：fhzm5658
％ PURPOSE : Demonstrate the differences between the following ％ filters on a simple DBN. ％ ％ 3) Particle Filter (PF) ％ 4) PF with Rao Blackwellisation (RBPF)
标签： Demonstrate differences the following
上传时间： 2016-01-07
上传用户：cjf0304
跳变马尔可夫模型状态估计的粒子滤波算法研究，本文在系统分析传统粒子滤波理论与应用问题的基础上，重点研究了基于跳变马尔可夫状态空间模型的粒子滤波算法。针对混合系统在二维离散状态情形下的混合状态估计问题，给出了基于Rao-Blackwellised粒子滤波的二维离散状态与连续状态的同步估计算法，一定程度上缓解了传统粒子滤波算法在高维状态空间估计中的失效问题，有效提高了状态估计的精度。应用数值仿真计算，对相关粒子滤波算法的性能进行了比较分析。结果表明，本文研究的算法能够有效完成二维离散状态与连续状态的同步估计，其中，二维离散状态的估计准确率达到了 96％。
上传时间： 2013-12-11
上传用户：qb1993225
采用网格地图SLAM算法，基于Rao-Blackwellized的粒子滤波器
上传时间： 2013-12-18
上传用户：semi1981
