多传感器加权观测融合Wiener反卷积滤波器  

Multisensor Weighted Measurement Fusion Wiener Deconvolution Filter

作　　者：崔崇信[1] 邓自立[2] 

机构地区：[1]黑龙江科技学院电气与信息工程学院,哈尔滨150027 [2]黑龙江大学电子工程学院,哈尔滨150080

出　　处：《科学技术与工程》2010年第30期7384-7388,共5页Science Technology and Engineering

基　　金：国家自然科学基金(60874063)资助

摘　　要：基于Kalman滤波方法,应用加权观测融合方法,提出了全局最优观测融合Wiener反卷积滤波器。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反卷积滤波器,而且明显减小计算负担,便于实时应用。一个四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 deconvolution filter is presented.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,so that it is suitable for real time application.A simulation example with four-sensor shows its effectiveness.

关 键 词：多传感器 加权观测融合 Wiener反卷积滤波器 Kalman滤波方法 

分 类 号：O211.64[理学—概率论与数理统计]