基于Kalman滤波的Wiener状态估值器(英文)

Wiener State Estimators Based on Kalman Filtering

应用经典稳态Kalman滤波理论提出了设计Wiener状态估值器的新方法,其原理是:基于在Wiener滤波器形式下的稳态Kalman滤波器和预报器及ARMA新息模型,由稳态最优非递推状态估值器的递推变形引出Wiener状态估值器.所提出的Wiener状态估值器可统一处理状态滤波、预报和平滑问题.它们具有ARMA递推形式,且具有渐近稳定性和最优性,仿真例子说明了它们的有效性.

Using classical steady-state Kalman filtering theory, a new approach of designing Wiener state estimators is presented, whose principle is that based on steady-state Kalman filter and predictor given in the Wiener filter form, and using the autoregressive moving average (ARMA) innovation model, the recursive version of non-recursive steady-state optimal state estimators yields the Wiener state estimators. The proposed Wiener state estimators can handle the state filtering, smoothing and prediction problems in a unified framework. They have the ARMA recursive form, and have asymptotic stability and optimality. A simulation example shows their effectiveness.