A reduced-order approach to filtering for systems with linear equality constraints
A reduced-order approach to filtering for systems with linear equality constraints
复制标题
具有线性等式约束的系统的降阶过滤方法
DOI：
10.1016/j.neucom.2016.02.020
复制
发表时间：
2016
期刊：
影响因子：
6
通讯作者：
Wen Chenglin
中科院分区：
文献类型：
--
作者：
Wen Chuanbo;Cai Yunze;Liu Yurong;Wen Chenglin
本文研究了一类具有线性等式约束的离散系统的滤波问题。所考虑的系统同时受到噪声和时变约束条件的影响。在温和的假设下设计了一种新的降阶滤波器，使所提出的滤波器的估计性能优于传统滤波器。利用重组后的约束信息，将原系统转化为降阶系统。提出了一种新的递归状态估计器，与现有的几种滤波器相比，它具有更高的估计精度。随后，进一步分析表明约束卡尔曼预测器是所提滤波器的一种特殊情况。最后，通过数值算例验证了该方法的有效性。
In this paper, the filtering problem is investigated for a class of discrete systems with linear equality constraints. The system under consideration is subject to both noises and time-varying constrained conditions. Attention is focused on the design of a new reduced-order filter under a mild assumption such that the estimation performance of the proposed filter outperforms those of the traditional filters. By using the reorganized constraint information, the original system is transformed to a reduced-order system. A new recursive state estimator is developed, which is proved to have higher estimation precision than several existing filters. Subsequently, further analysis shows that the constrained Kalman predictor is a special case of the proposed filter. Finally, a numerical example is employed to demonstrate the effectiveness of our approach.