ARMA信号自校正信息融合Wiener滤波器

Self-tuning Information Fusion Wiener Filter for ARMA Signals

对于带多传感器的含有未知模型参数和噪声统计的ARMA信号,应用现代时间序列分析方法,基于ARMA新息模型的在线辨识,提出了多传感器自校正信息融合Wiener滤波器。它具有渐近最优性,且可统一处理滤波、平滑和预报问题。一个跟踪系统的仿真例子说明了其有效性。

By the modern time series analysis method, based on on-line identification of autoregressive moving average (ARMA) imovation model, a multisensor self-tuning information fusion Wiener filter is presented for the ARMA signals with multisensor, and with unknown model parameters and noise statistics. It has asymptotic optimality, and can handle the filtering, smoothing and prediction problems in a u-nified framework. A simulation example for tracking systems shows its effectiveness.