一种基于模型误差预测的UKF方法

UnscentedKalman滤波器（UKF）对本质非线性系统具有估计精度高、收敛速度快和容易实现等优点,但是对系统的模型误差比较敏感。针对这一问题,提出了一种基于模型误差预测的UKF方法,称为PUKF（PredictiveUnscentedKalmanFilter）。它利用非线性预测滤波器（NPF）的模型误差预测过程,能够对不准确的系统模型进行实时修正,弥补了UKF方法的不足。仿真结果表明,相对于原始的UKF方法,新方法从滤波精度、收敛速度和收敛的稳定性等几个方面,显著提高了非线性滤波的性能。PUKF可适用于模型不确定、非线性较强系统的滤波。

UKF Method Based on Model Error Prediction

For essentially nonlinear systems, the Unscented Kalman Filter (UKF) has some advantages such as high estimation precision, fast convergence and easy accomplishment. But the UKF is sensitive to model error of systems. To address this problem, a new UKF method based on model error prediction (MEP) is proposed, which is called Predictive Unscented Kalman Filter (PUKF). The new filter utilizes the MEP process of Nonlinear Predictive Filter (NPF), which can adjust the inaccurate model in real time and thus remedy the shortage of the UKF. Theory analysis and simulation results demonstrate that the new filtering method remarkably improves the efficiency of nonlinear filtering. Compared with the UKF, the new filter significantly improves the performance in precision, convergence speed and stability. So PUKF is applicable to uncertain and high nonlinear system.