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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 tracking algorithm based on “target state estimation and filtering ” within the target tracking method. First, the theoretical basis of this algorithm is introduced: Bayesian filter and Monte Carlo method, and the limitation of Kalman filter is pointed out. The linear system and the Gaussian distribution are satisfied, and then the theoretical framework of particle filter is deduced, and how to apply it to the target tracking is explained.