粒子滤波理论是近年来跟踪领域的热门研究课题。在该领域，传统的卡尔曼（Kalman）滤波器是非常经典的运动目标跟踪工具。然而经典亦有其弊端，卡尔曼滤波对于非线性及非高斯环境下的工作能力相当无力。为解决这一问题，本文提出了一种基于粒子滤波的目标跟踪方法。其核心为以粒子(一种随机样本，携带权值)来表示后验概率密度，从而得到基于物理模型的近似最优数值解，其优点在于能在追踪的过程中实现更高的精度和更快的收敛速度等。粒子滤波通过加权计算这些带有权重的随机样本来得到目标的近似的运动状态，因此对于非高斯和非线性的环境有着较强的鲁棒性（对特性或参数扰动的不敏感性）。本文将粒子滤波运用于视频跟踪，与传统卡尔曼滤波器进行对比实验，通过实验结果进一步说明粒子滤波对卡尔曼滤波的优势，因此可以广泛的替代传统卡尔曼滤波器，应用于各个跟踪系统中。-Because in non-linear non-Gaussian environment the performance of traditional Kalman Filter in tracking of moving targets is very poor, the paper uses particle filter to track the moving target. Particle filter does not involve linearization around current estimates but rather represent the desired distributions by discrete random measures, which are composed of weighted particles. It has a high accuracy and a rapid convergence. The theory of target tracking based on particle filter is to use these weighted particles to estimate the states of targets. The simulation results of the target model show that in the non-linear non-Gaussian environment, the performance of the particle filter is better than extended Kalman Filter. Finally, we use the particle filter in video tracking, the experimental results further show that in the non-linear non-Gaussian environment the particle filter has a better tracking performance. Particle filter technology can be widely used for air to air, air-groun