自校正解耦融合Kalman滤波器及其收敛性  

Self-tuning Decoupled Fusion Kalman Filter and Its Convergence

作　　者：王强[1] 王伟玲[1] 邓自立[1] 

机构地区：[1]黑龙江大学自动化系,哈尔滨150080

出　　处：《科学技术与工程》2009年第4期836-841,共6页Science Technology and Engineering

基　　金：国家自然科学基金(60874063)资助

摘　　要：对带未知噪声统计的多传感器系统,提出了基于相关方法的噪声统计在线估值器,进而提出了自校正Riccati方程和自校正Lyapunov方程。在按分量标量加权线性最小方差最优信息融合准则下,提出了自校正分量解耦融合Kalman滤波器,并用动态误差系统分析(DESA)方法证明了它收敛于最优分量解耦融合稳态Kalman滤波器,因而具有渐近最优性,它的精度比每个局部自校正Kalman滤波器精度高,且算法简单,便于实时应用。一个目标跟踪系统的仿真例子说明了其有效性。For the multisensor systems with unknown noise statistics,the on-line noise statistics estimators are presented based on the correlated method,and the self-tuning Riccati equation and Lyapunov equation are also presented. Under the linear minimum variance optimal information fusion criterion weighted by scalars for components , a self-tuning component decoupled fusion Kalman filter is presented, and it is proved by the dynamic error system analysis （DESA） method that it converges to the optimal component decoupled fusion steady-state Kalman filter,so that it has the asymptotic optimality. Its accuracy is higher than that of each local self-tuning Kalman filter,moreover algorithm is simple,and is suitable for real time applications. A simulation example for a target tracking system shows its effectiveness.

关 键 词：多传感器信息融合 解耦融合 RICCATI方程 噪声统计估计 自校正Kalman滤波器 

分 类 号：O211.64[理学—概率论与数理统计]