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基于粒子滤波的一种改进的资料同化方法
Improved particle filter in data assimilation
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摘要: 针对在粒子数较少时传统的集合卡尔曼滤波和粒子滤波方法不能有效表征后验概率密度函数（PDF）的问题,提出了一种改进的粒子滤波方法.主要思想是在预测步之后引入更新步,并且将观测时刻与非观测时刻的同化分析进行区别处理.对典型的低维和高维混沌系统的仿真结果表明：改进粒子滤波方法是一种非常有效的估计非线性非高斯随机系统状态的方法.Abstract: 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.
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