按对角阵加权信息融合Kalman滤波器

Information fusion in Kalman filter weighted by diagonal matrices

中文关键词  多传感器  按对角阵加权信息融合Kalman滤波器  实时信息融合  解耦信息融合Kalman滤波器  Lyapunov方程  现代时间序列分析方法

英文关键词  multisensor  information fusion in Kalman filter weighted by diagonal matrices  real time information fusion  decoupled information fusion in Kalman filter  Lyapunov equation  modern time series analysis method

中文摘要

      为了克服按矩阵加权信息融合非稳态Kalman滤波器的在线计算负担大的缺点,和按标量加权融合Kalman滤波器精度较低的缺点,应用现代时间序列分析方法,提出了按对角阵加权的线性最小方差多传感器信息融合稳态Kalman滤波器.它等价于状态分量按标量加权信息融合Kalman滤波器,实现了解耦信息融合Kalman滤波器.它的精度和计算负担介于按矩阵和按标量加权融合器两者之间,且便于实时应用.为了计算最优加权,提出了计算稳态滤波误差方差阵和协方差阵的Lyapunov方程.一个三传感器的雷达跟踪系统的仿真例子说明了其有效性.

英文摘要

      In order to overcome the drawbacks that the information fusion in non-steady-state Kalman filter weighted by matrices requires a large on-line computational burden,and that the accuracy of the fused Kalman filter weighted by scalars is low,a multisensor information fusion in steady-state Kalman filter weighted by diagonal matrices is presented by the modern time series analysis method.It is equivalent to the information fusion in Kalman filters weighted by scalars for the state components,so that the decoupled information fusion in Kalman filters is achieved.Its accuracy and computational burden are between those weighted by matrices and weighted by scalars.It is suitable for real time applications.In order to compute the optimal weights,the Lyapunov equations for computing the filtering error variance and covariance matrices are also presented.A simulation example for an radar tracking system with three-sensor shows its effectiveness.