随着这些年计算机硬件水平的发展, 计算速度的提高, 源自序列蒙特卡罗方法的蒙特卡罗粒子滤波方法的应用研究又重新活跃起来。本文的这种蒙特卡罗粒子滤波算法是利用序列重要性采样的概念, 用一系列离散的带权重随机样本近似相

应的概率密度函数。由于粒子滤波方法没有像广义卡尔曼滤波方法那样对非线性系统做线性化的近似, 所以在非线性状态估计方面比广义卡尔曼滤波更有优势。在很多方面的应用已经逐渐有替代广义卡尔曼滤波的趋势。-With the years the level of computer hardware development, the speed of calculation, derived from the sequence of the Monte Carlo method, Monte Carlo particle filter method applied research has once again become active again. In this paper, this kind of Monte Carlo particle filter is to use the concept of sequence of the importance of sampling, using a series of discrete random sample with weights similar to the corresponding probability density function. Since the particle filtering method is not as broad as Kalman filtering method for nonlinear system to do linear approximation, nonlinear state estimation in the generalized Kalman filter than an advantage. Applications in many areas has been gradually generalized Kalman filter has an alternative trend.