贝叶斯自主滤波器 Bayesian bootstrap filter
The particle filter is used to apply a recursive Bayesian filter based on the propagation of sample set over time, maintain multiple hypotheses at the same time and use a stochastic motion model to predict the position of the object.
粒子滤波器是一种基于传播样本集的递归贝叶斯滤波器,同时它保持多重假设以及使用随机运动模型预测目标位置。
参考来源 - 基于均值漂移和粒子滤波的目标跟踪算法研究·2,447,543篇论文数据，部分数据来源于NoteExpress
这种新的贝叶斯滤波算法是粒子滤波器与划分采样技术和假设计算的有机结合。
The proposed algorithm is a combination of the partition sampling technique and hypothesis calculations with the particle filter.
通过假设预测方位和实测方位差值服从零均值的高斯分布，利用贝叶斯理论来修正各滤波器的权重。
And the weight of each filter is updated using Bayes theory based on the assumption that the difference between estimate and measurement bearings obeys Gaussian distributions with zero mean error.
介绍了作为粒子滤波理论基础的递推贝叶斯估计的基本概念，说明了重要性函数对于粒子滤波器的设计是至关重要的。
The principle of Recursive Bayesian estimation was introduced which was the basis of Particle filter, and the significance of importance function to the design of particle filter was illustrated.
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