中文摘要：

针对非线性、非高斯系统状态估计问题，提出了一种基于重要密度函数的改进粒子滤波器一马尔可夫链蒙特卡罗容积粒子滤波器．在状态转移概率的基础之上综合考虑了当前的量测信息，利用容积卡尔曼滤波对每个采样粒子进行估计，使得重要密度函数更加贴近于真实后验；同时为避免粒子贫乏，在重采样后加入马尔可夫链蒙特卡罗步骤．理论分析和实验仿真表明：马尔可夫链蒙特卡罗容积粒子滤波器的性能要优于容积粒子滤波器以及其他参照滤波器．

英文摘要：

A novel improved particle filter based on sequential importance sampling, Monte Carlo Markov Chain （MCMC） cubature particle filter, is proposed for the estimation of non-linear non-Gaussian system. Each particle is estimated by means of cubature Kalman filter. The importance density function gets closer to the real posterior after taking the current observation into consideration on the basis of state transition. MCMC step is added after the selection. The theoretical analysis and the simulation experiment show the cubature particle filter vefforms much better than the other Parallel filters.