耦合集合卡尔曼滤波的非线性参数估计

准确的参数对于非线性模型或者函数有极为重要的意义。但是在实际应用中,输入的参数常常带有一定的偏差,因此在预测中非线性模型(函数)往往得不到满意的结果。把集合卡尔曼滤波方法引入到非线性模型(函数)的参数估计中,并采用基于联合状态向量的方法在同化时刻同时更新模型(函数)的状态和参数,该方法可以通过同化观测数据,动态地调整参数和结果,使得参数能够自适应地变化,同时也能较好地释放积累的模型误差。将该方法应用于随时间变化的二维非线性模型(函数)中,实验表明,该方法能够准确地估计出模型参数值,同时也表现出很好的鲁棒性和自适应性。

Nonlinear Parameter Estimation by Coupling Ensemble Kalman Filter

It is very important for model or function to have exact input parameters. However, the input parameters often have some error in practical application. The parameter error will lead to larger model error in prediction of non-linear model (parameter). Ensemble Kalman filter is introduced into non-linear model (parameter) for parameter estimation. And joint state vector is used to update model parameter and state in the same assimilation time. The method can dynamically adjust model parameters and states according to changing environment by assimilating observation data. And more importantly, it can release the accumulated model error. The method is applied to two-dimension non-linear model which changes with time step. Studies show that the method can obtain ideal results in parameter estimation. It also has good performance in robustness and self-adaption.