Recently , withthe rapid improvement of performance of digital processor , sequential monte carlo ( smc ) method has a wide range of application in engineering , especially in signal processing , statistics , and econometrics etc . the time varying systems can be stated in the form of a dynamic state space model . for linear models and gaussian noise , the kalman filter provides analytical expressions for posterior filtering 一般的时变系统都可以被看作是一动态状态空间模型,对于线性高斯模型,卡尔曼滤波可以给出后验密度函数的解析解;而对于非线性非高斯模型,我们则无法得到它的解析解,在这种情况下则可以使用序列蒙特卡罗方法来对其进行近似。