多传感器全局最优加权观测融合Wiener信号滤波器

Multisensor Globally Optimal Weighted Measurement Fusion Wiener Signal Filter

基于Kalman滤波,应用加权观测融合方法,对于带白色观测噪声的单通道ARMA信号,提出了全局最优多传感器观测融合Wiener信号滤波器。可统一处理信号融合滤波、平滑和预报问题。同集中式观测融合方法和分布式状态融合方法相比,不仅可获得全局最优Wiener信号滤波器,而且明显减小计算负担,便于实时应用。一个三传感器加权观测融合仿真例子说明了其有效性。

Based on the Kalman filtering method, applying the weighting measurement fusion method, a globally optimal multisensor measurement fusion Wiener signal filter is presented for single channel ARMA signals with white measurement noise. It can handle the signal fused filtering, smoothing and prediction problems in a u- nified framework. Compared with the centralized measurement fusion method and the decentralized state fusion method, not only the globally optimal Wiener signal filter can be obtained, but also the computational burden can obviously be reduced and it is suitable for real time applications. A simulation example with three--sensor shows its effectiveness.