广义系统稳态Kalman估值器

用现代时间序列分析方法,提出了广义离散线性随机系统稳态Kalman滤波、平滑 和预报的一种统一格式,给出了稳态Kalman估值器增益新算法,避免了求解Riccati方程.为 保证估值器的渐近稳定性,给出了选择初始估值的公式.仿真例子说明了所提出的结果的有 效性.

STEADY-STATE KALMAN ESTIMATORS FOR SINGULAR SYSTEMS

Using the modern time series analysis method, a unifying framework of steady state Kalman filtering, smoothing and prediction for singular discrete linear stochastic systems, is presented. A new algorithm of steady state Kalman estimator gain is given, where the solution of the Riccati equation is avoided. In order to ensure the asymptotic stability of the estimator, a formula of setting the initial estimate is given. A simulation example shows the effectiveness of the proposed results.