| 随机控制系统稳态Kalman滤波器新算法 |
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| 引用本文： | 邓自立, 刘玉梅. 随机控制系统稳态Kalman滤波器新算法. 自动化学报, 2000, 26(1): 74-78. |
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| 作者姓名： | 邓自立 刘玉梅 |
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| 作者单位： | 1.黑龙江大学应用数学研究所,哈尔滨;;;2.中国民用航空学院航行系,天津 |
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| 基金项目： | 国家自然科学基金资助项目!( 6 9774 0 1 9) |
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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 |
| 收稿时间： | 1997-09-29 |
| 修稿时间： | 1997-09-29 |
NEW ALGORITHMS OF STEADY STATE KALMAN FILTER FOR STOCHASTIC CONTROL SYSTEMS |
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| Deng Zili, Liu Yumei. New Algorithms of Steady-State Kalman Filter for Stochastic Control Systems. ACTA AUTOMATICA SINICA, 2000, 26(1): 74-78. |
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| Authors: | DENG Zili LIU Yumei |
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| Affiliation: | 1. Institute of Applied Mathematics,Heilongjiang University,Harbin;Department of Aviation,Civil Aviation Institute of China,Tianjin |
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| Abstract: | Using the modern time series analysis method, based on the controlled autoregressive moving average(CARMA) innovation model, two new algorithms of steady state Kalman 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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| Keywords: | Stochastic control systems algorithms of steady state Kalman filter gain asymptotic stability modern time series analysis. |
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