粒子滤波理论框架及在目标跟踪中的应用

Framework of particle filter and its application in object tracking

重点研究了目标跟踪方法内的基于"目标状态估计、滤波"的跟踪算法,首先介绍了该类算法的理论基础:贝叶斯滤波器和Monte Carlo方法,指出Kalman滤波器的局限性:满足线性系统和高斯分布,进而推导了粒子滤波的理论框架,并就其如何应用于目标跟踪进行了阐述。

This paper focuses on the research of object tracking method based on the target state estimation,filtering tracking algorithm.Firstly,introduces the theoretical basis of this class of algorithms:Bayesian filter and Monte Carlo method,points out the limitations of the Kalman filter:needing to meet linear system and Gauss distribution,and then the framework of particle filter theory is derived,and its application in target tracking is discussed in this paper.