粒子滤波方法由于能够灵活地处理非线性非高斯系统而被广泛地应用。
Particle filter is widely used because of its flexibility to deal with the nonlinear non-Gaussian systems.
目前在信息融合领域广泛使用的融合算法是卡尔曼滤波，它在线性高斯模型下能得到最优估计，但在非线性非高斯模型下则无法应用。
The Kalman Filter is widely applied in the Information Fusion at the present, which can get the optimal estimate in the Linear-Gaussian model, but not applied in the nonlinear and non-Gaussian model.
由于我们实际生活中的系统基本上都是非线性的，因此本文研究的是专门用于非线性非高斯系统跟踪的粒子滤波算法(PF)的基本原理及其具体应用。
Since the real life systems basically are nonlinear, so this paper study the basic principles and specific applications of Particle Filter (PF) specially used for non-linear non-Gaussian tracking.
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