广义离散系统多传感器信息融合Kalman滤波器  

Multi-sensor Information Fusion Steady-state Kalman Filter for Descriptor Discrete-time Systems

作　　者：石莹[1] 段广仁[1] 

机构地区：[1]哈尔滨工业大学控制理论与制导技术研究中心

出　　处：《控制与决策》2006年第3期339-342,共4页Control and Decision

基　　金：国家自然科学基金项目(60374024)

摘　　要：考虑了广义离散随机线性系统的多传感器信息融合状态估计问题.在广义系统无脉冲的假设条件下,通过等价变换将其转化为正常系统.应用经典Kalman滤波方法,在线性最小方差信息融合准则下,提出了按矩阵加权的广义系统多传感器信息融合稳态Kalman状态滤波器.仿真结果说明了算法的有效性.The problem of multi-sensor information fusion state estimation for descriptor discrete-time stochastic linear systems is considered. The descriptor system under condideration is subject to the pulse-free hypothesis and is converted into a normal system by an equivalent transformation. Based on classical Kalman filtering method, a multi-sensor information fusion stead-state Kalman filter weighted by matrices is proposed under linear least variance information fusion criterion. Simulation results show the effectiveness of the proposed algorithm.

关 键 词：广义随机系统 状态估计 多传感器信息融合 KALMAN滤波 Riecati方程 

分 类 号：O211.64[理学—概率论与数理统计]