| 自适应Kalman滤波器及其应用 |
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| 引用本文： | 邓自立,李北新.自适应Kalman滤波器及其应用[J].自动化学报,1992,18(4):408-413. |
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| 作者姓名： | 邓自立 李北新 |
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| 作者单位： | 1.黑龙江大学应用数学研究所,哈尔滨; |
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| 摘 要： | 对于带未知噪声统计的单输出系统,本文提出了一种新的自适应Kalman滤波器.应用
现代时间序列分析方法,基于ARMA新息模型的滑动平均(MA)参数的在线辨识,提出了
稳态最优Kalman滤波器增益估计的一种新算法,比Mehra的算法简单.同时还提出了辨
识滑动平均(MA)模型参数的一种新的自适应Kalman滤波算法.此外,给出了在雷达跟
踪系统中的应用,且仿真结果说明了本文算法的有效性.
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| 关 键 词： | 自适应Kalman滤波 稳态滤波增益估计 ARMA新息模型 辨识 雷达跟踪系统 |
| 收稿时间： | 1989-2-20 |
An Adaptive Kalman Filter and ist Application |
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| Deng Zili,Li Beixin.An Adaptive Kalman Filter and ist Application[J].Acta Automatica Sinica,1992,18(4):408-413. |
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| Authors: | Deng Zili Li Beixin |
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| Affiliation: | 1.Institute of Applied Mathematics,Heilongjiang University;Institute of Science Research and Design,Northeast Management Centre of Carrying Crude Oil |
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| Abstract: | A new adaptive Kalman filter is presented for the single output system with unknown noise statistics. By a time series analysis, a new and simpler estimation algorithm for the gain of the steady-state optimal Kalman filter is given. A new adaptive Kalman filtering algorithm is also given for identifying the parameters of moving average (MA) model. An application to a radar tracking system is given to show the usefulness of the proposed new algorithms. |
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| Keywords: | Adaptive Kalman filtering steady-state Kalman filter gain estimation AR- MA innovation model identification radar tracking system |
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