多传感器ARMA信号观测融合Wiener滤波器

Multisensor ARMA Signal Measurement Fusion Wiener Filter

利用现代时间序列分析方法,基于ARMA新息模型,提出了多传感器加权观测融合Wiener信号滤波器。可统一处理信号融合预报、滤波和平滑问题。同集中式观测融合方法和分布式状态融合方法相比,不仅可得到全局最优Wiener信号滤波器,而且可显著地减小计算负担,便于实时应用。一个两传感器位置跟踪系统的仿真例子说明其有效性。

By using the modern time series analysis method,based on the autoregressive moving average （ARMA） innovation model,the multisensor weighted measurement fusion Wiener signal filter is presented. It can handle the signal fused prediction,filtering and smoothing problems in a unified framework. Compared with the centralized measurement fusion method and the decentralized state fusion method,not only it gives the globally optimal Wiener signal filter,but also it can obviously reduce the computational burden,so that it is suitable for real time applications. A simulation example of a location tracking system with two-sensor shows its effectiveness.