[1]宋文忠.带随机限定记忆模型的最优线性滤波[J].东南大学学报(自然科学版),1979,9(3):112-125.[doi:10.3969/j.issn.1001-0505.1979.03.010]
 Soong Wen-Zhong.Optimal Linear Filter with a Stochastic Finite-Memory Model[J].Journal of Southeast University (Natural Science Edition),1979,9(3):112-125.[doi:10.3969/j.issn.1001-0505.1979.03.010]
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带随机限定记忆模型的最优线性滤波()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
9
期数:
1979年第3期
页码:
112-125
栏目:
本刊信息
出版日期:
1979-09-20

文章信息/Info

Title:
Optimal Linear Filter with a Stochastic Finite-Memory Model
作者:
宋文忠
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Author(s):
Soong Wen-Zhong
关键词:
最优线性滤波 最小均方误差 记忆模型 记忆矢量 卡尔曼滤波器 最优预测 限定记忆 输出信号 数学模型 记忆量
分类号:
+
DOI:
10.3969/j.issn.1001-0505.1979.03.010
摘要:
本文用对象输出信号的N个记忆量来构成随机限定记忆模型,去代替卡尔曼滤波器中所使用的对象状态方程模型,作为一步最优预测的依据。在此基础上推导出离散系统的最小均方误差的滤波公式组。在不能获得精确的对象状态方程的情况下,可用本文提出的滤波方法对对象的状态变量作出估计。
Abstract:
Using a limited number of sampled-data of system output, a stochastic finite-memory model may be established to take the place of the system state equation used in the Kalman filter to perform one-step optimal prediction. On this basis, two sets of equations of the "minimum mean-square-error" discrete filter are derived. When the system state equation is unknown, the system state can be estimated with the method developed in this paper.

相似文献/References:

[1]黄爽,江彬,高西奇.自适应MIMO系统中的最优线性疏散码设计[J].东南大学学报(自然科学版),2008,38(4):569.[doi:10.3969/j.issn.1001-0505.2008.04.005]
 Huang Shuang,Jiang Bin,Gao Xiqi.Design of optimal linear dispersion code in adaptive MIMO system[J].Journal of Southeast University (Natural Science Edition),2008,38(3):569.[doi:10.3969/j.issn.1001-0505.2008.04.005]

更新日期/Last Update: 2013-05-01