Improved particle filter in data assimilation
基于粒子滤波的一种改进的资料同化方法

Owing to the fact that standard particle filter and ensemble Kalman filter can not efficiently represent the posterior probability density function (PDF), an improved particle filter is proposed. In this algorithm, an innovation step is introduced after the prediction step, and the analyses of non-observation time and observation time are treated separately. The numerical simulations of a low- and a high-dimensional systems show that this new particle filter can follow the true state of a highly nonlinear non-Gaussian system very well.