Linear minimum-mean-square error estimation of Markovian jump linear systems with randomly delayed measurements
Linear minimum-mean-square error estimation of Markovian jump linear systems with randomly delayed measurements
复制标题
具有随机延迟测量的马尔可夫跳跃线性系统的线性最小均方误差估计
DOI：
10.1049/iet-spr.2013.0431
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发表时间：
2014-08
影响因子：
1.7
通讯作者：
Pan Quan
中科院分区：
文献类型：
--
作者：
Yang Yanbo;Liang Yan;Yang Feng;Qin Yuemei;Pan Quan
研究了具有随机时滞观测的离散马尔可夫跳跃线性系统的状态估计问题。这里，根据不同可能的延迟步长，将延迟建模为不同数目的二进制随机变量的组合。在实际延迟测量方程中，多个相邻阶跃测量噪声是相关的。由于测量时延的随机性，将估计模型改写为具有随机参数的离散时间系统，并由具有模式不确定性的所有模式重构增广状态。对于该系统，在广义框架下，根据正交性原理，在递归结构中得到了一种新的增广状态的线性最小均方误差估值器。由于测量方程中多个相邻阶跃噪声之间的相关性，还需要估计或计算当前每一步中对应的先前时刻的测量噪声和相关的二阶矩矩阵。对可能存在测量延迟的算例进行了仿真，验证了该方法的有效性。
This study presents the state estimation problem of discrete-time Markovian jump linear systems with randomly delayed measurements. Here, the delay is modelled as the combination of different number of binary stochastic variables according to the different possible delay steps. In the actually delayed measurement equation, multiple adjacent step measurement noises are correlated. Owing to the stochastic property from the measurement delay, the estimation model is rewritten as a discrete-time system with stochastic parameters and augmented state reconstructed from all modes with their mode uncertainties. For this system, a novel linear minimum-mean-square error (LMMSE, renamed as LMRDE) estimator for the augmented state is derived in a recursive structure according to the orthogonality principle under a generalised framework. Since the correlation among multiple adjacent step noises in the measurement equation, the measurement noises and related second moment matrices of corresponding previous instants in each current step are also needed to be estimated or calculated. A numerical example with possibly delayed measurements is simulated to testify the proposed method.
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影响因子：
12.3
作者：
Hui Zhang;Yang Shi;Mingxi Liu
通讯作者：
Hui Zhang;Yang Shi;Mingxi Liu
DOI：
10.1109/tac.2008.2007181
发表时间：
2007-07
期刊：
2007 American Control Conference
影响因子：
--
作者：
M. Terra;J. Ishihara;Gildson Jesus
通讯作者：
M. Terra;J. Ishihara;Gildson Jesus
影响因子：
6.8
作者：
M. Terra;J. Ishihara;Antonio P. Junior
通讯作者：
M. Terra;J. Ishihara;Antonio P. Junior
影响因子：
6
作者：
C. Ahn
通讯作者：
C. Ahn
影响因子：
1.7
作者：
Wenling Li;Y. Jia
通讯作者：
Wenling Li;Y. Jia