带乘性噪声广义系统多传感器最优滤波融合算法  

Multi-Sensor Information Fusion Filtering Algorithms for Singular Systems with Multiplicative Noises

作　　者：褚东升[1] 付东飞[1] 

机构地区：[1]中国海洋大学工程学院,山东青岛266100

出　　处：《中国海洋大学学报（自然科学版）》2010年第7期149-154,共6页Periodical of Ocean University of China

基　　金：国家高技术研究发展计划项目(2006AA09Z115);国家自然科学基金项目(60704023)资助

摘　　要：研究带乘性噪声广义系统的多传感器信息融合状态估计问题。在系统正则的假设条件下,通过受限等价变换将广义系统变换为2个降阶子系统,进而将针对原系统的信息融合状态估计转化为2个耦合的非广义子系统的多传感器信息融合状态估计,然后分别推导出集中式与分布式滤波融合算法,这2种算法在数学上完全等价,均在线性最小方差意义下最优。考虑到分布式滤波融合算法的优点,重点讨论了该算法,仿真实验验证了该融合算法的有效性。Multi-sensor information fusion state estimation problem for multi-channel stochastic singular systems with multiplicative noise is studied. The singular system under consideration is subject to the regular hypothesis and is transformed into two reduced-order subsystems by restricted equivalent transformation. Based on this transformation, then the information fusion state estimation problem for the original systems is converted into the state estimation problem of the two coupled normal subsystems. Two filtering algorithms are presented in this paper, one is centralized and the other is decentralized, both of which are optimal in the sense of linear minimum-variance. Considering the advantage in the application, we chiefly discuss the decentralized information fusion algorithms. Finally, simulation results show the effectiveness of the proposed algorithm.

关 键 词：广义系统 乘性噪声 多传感器信息融合 滤波 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]