随机控制系统稳态Kalman滤波器新算法
New Algorithms of Steady-State Kalman Filter for Stochastic Control Systems
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摘要: 应用现代时间序列分析方法,基于受控的自回归滑动平均(CARMA)新息模型,提 出了随机控制系统稳态Kalman滤波器增益的两种新算法,避免了求解Riccati方程.为保证 滤波器的渐近稳定性,给出了选择滤波初值的两个公式.仿真例子说明了新算法的有效性.
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关键词:
- 随机控制系统 /
- 稳态Kalman滤波器增益算法 /
- 渐近稳定性 /
- 现代时间序列分析
Abstract: Using the modern time series analysis method, based on the controlled autoregressive moving average (CARMA) innovation model, two new algorithms of steady-state Kalrnan filter gain for stochastic control systems are presented, where the solution of the Riccati equation is avoided. In order to ensure the asymptotic stability of the filter, two formulae of setting initial filtering estimate are given. A simulation example shows the effectiveness of the new algorithms. -
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