基于UKF的参数和状态联合估计

对于因模型参数失配造成的非线性系统状态估计不准确现象，采用基于不敏卡尔曼滤波（UKF）的参数和状态联合估计方法，即将未知模型参数和状态组成增广的状态向量，用UKF同时获得参数和状态估计值。通过一个离散非线性随机系统的蒙特卡洛仿真，总结滤波器参数对联合估计器性能的影响及参数选择规律。最后将该方法应用于一个典型的化工反应过程，获得了较好的效果。

Joint Estimation of the Parameter and State Based on UKF

The mismatch of model parameter would lead to the inaccuracy of the estimation of states for nonlinear systems. This paper proposes a joint estimation approach based on Unscented Kalman Filter, in which both parameters and states are simultaneously estimated by means of the argument state vector composed of the unknown model parameters and states. By utilizing the Monte Carlo simulation for a discrete nonlinear stochastic system, the influence of the filter parameters on the performance of the joint estimator is analyzed and the updating rule of filter parameters is obtained. Finally, this approach is successfully applied to a typical chemical reaction process.