利用MKLD准则的自适应PF算法设计及其应用

Design of Adaptive Particle Filtering Algorithm Based on MKLD Criteria and Its Application

为降低PF算法的计算量,提出了基于最大Kullback-Leibler距离(MKLD)准则的PF-AMCMC算法。该算法可在自适应地选择粒子数的前提下,同时自适应地选择粒子滤波算法中MCMC移动步骤实施的时刻,在保证一定的状态估计精度的条件下,减少粒子滤波的计算量。大量的数值试验和GPS/DR组合导航仿真试验表明,本文提出的算法较标准粒子滤波算法在克服粒子滤波计算量大的缺陷方面有显著的效果,且获得了精度更高的状态估计。

This paper presents a new algorithm named PF-AMCMC based on Maximum Kullback- Leibler distance （abbreviated as MKLD） criterion. This algorithm can adaptively choose the number of particles and at the same time select the implementation moment of MCMC movement, and reduces the computational complexity under conditions guaranteeing the accuracy of state estimation. The results of computational experiments and a GPS / DR integrated navigation simulation experiment show that the improved particle filtering methods proposed in this paper have a better performances for state estimation than other approaches.