粒子滤波算法源于Montecarlo的思想，即以某事件出现的频率来指代该事件的概率。因此在滤波过程中，需要用到概率如P(x)的地方，一概对变量x采样，以大量采样的分布近似来表示P(x)。因此，采用此一思想，在滤波过程中粒子滤波可以处理任意形式的概率，而不像Kalman滤波只能处理高斯分布的概率问题。他的一大优势也在于此。(A number of prognostics approaches have been proposed in
the literature in support of PrM [6]. Among these, Particle Filtering (PF) is emerging as a powerful model-driven technique,
capable of robustly predicting the future behavior of the probability mass distribution that describesthe uncertainty in the actual degradation state of the equipment)