【摘要】 应用现代时间序列分析方法,基于自回归滑动平均(ARMA)新息模型,对于带未知模型参数和噪声方差的两传感器反卷积系统,提出了自校正信息融合白噪声Wiener反卷积滤波器。它具有渐近最优性。一个Bernoulli-Gaussian白噪声反卷积的仿真例子说明了其有效性。更多还原

【Abstract】 By the modern time series analysis method, based on the on-line identification of the autoregressive moving aver-age (ARMA) innovation model, a self-tuning information fusion white noise Wiener deconvolution filter is presented for two-sensor deconvolution systems with unknown model parameters and unknown noise variances. It has asymptotic optimality. Asimulation example for Bemoulli-Gaussian white noise deconvolution shows its effectiveness.更多还原