Optimal linear estimation for systems with multiplicative noise uncertainties and multiple packet dropouts
Optimal linear estimation for systems with multiplicative noise uncertainties and multiple packet dropouts
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DOI：
10.1049/iet-spr.2012.0065
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
Jing Ma;Shu-Li Sun
中科院分区：
文献类型：
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作者：
Jing Ma;Shu-Li Sun
本研究涉及线性离散时间随机系统的最优线性估计问题，该系统具有状态和测量矩阵中的乘性噪声不确定性以及从传感器到估计器的多个数据包丢失。基于投影理论，在线性最小方差意义上推导了包括滤波器、预测器和平滑器在内的最优线性估计器。在不存在随机不确定性和/或数据包丢失的情况下，可以获得相应的结果作为所提出的估计器的特殊情况。还分析了稳态特性。获得了稳态估计量存在的充分条件。它们可以离线计算。因此他们的在线计算成本降低了。给出了仿真示例来证明所提出的估计器的有效性。
This study is concerned with the optimal linear estimation problem for linear discrete-time stochastic systems with multiplicative noise uncertainties in state and measurement matrices and with multiple packet dropouts from a sensor to an estimator. Based on the projection theory, the optimal linear estimators including filter, predictor and smoother are derived in the linear minimum variance sense. In the absence of stochastic uncertainties and/or packet dropouts, the corresponding results can be obtained as the special cases of the proposed estimators. Steady-state property is also analysed. A sufficient condition for the existence of the steady-state estimators is obtained. They can be computed offline. So they have the reduced online computational cost. Simulation examples are given to demonstrate the effectiveness of the proposed estimators.