多传感器分布式信息融合Wiener信号滤波器

Multisensor Distributed Information Fusion Wiener Filter

应用Kalman滤波方法,对于带白色和有色观测噪声单通道ARMA信号,基于Riccati方程,在线性最小方差按标量加权的最优信息融合准则下,提出了多传感器分布式信息融合Wiener信号滤波器。提出了计算局部滤波误差间的互协方差的Lyapunov方程,可用于计算最优加权系数。同单传感器情形相比,可提高滤波精度。一个三传感器信息融合Wiener跟踪滤波器的仿真例子说明了其有效性。

Using Kalman filtering method, based on Riccati equation, under the linear minimum variance optimal information fusion criterions weighted by scalars, for the single channel ARMA signals with white and colored measurement noises, the multisensor distributed information fusion Wiener filters is presented. The Lyapunov equations of computing local filtering error covariances are presented, which are applied to obtain the optimal weighting coefficients. Compared with the single sensor case, it can improve the filtering accuracy. A simulation of three-sensors information fused tracking filter shows its effectiveness.