带有色观测噪声的多传感器ARMA模型信息融合辨识

对带已知有色观测噪声的未知自回归滑动平均模型(ARMA)模型,提出了一种两段信息融合辨识方法:第一段用递推辅助变量(RIV)算法得到自回归(AR)参数的局部和融合一致估值,第二段用Gevers-Wouters算法和用伪逆求解线性方程组方法得到滑动平均(MA)参数和噪声方差的局部和融合一致估值。该方法可用于语言增强信号处理问题。一个仿真例子说明其有效性。

Information Fusion Indentification of Mutisensor ARMA Model with Colored Measurement Noise

For the unknown autoregressive moving average(ARMA) model with known colored measurement noise,a two-stage information fusion identification method is presented:in the first stage,the local and fused estimates of the autoregressive(AR) paraments are obtained by the recursive instrumental variable(RIV),and in the second stage,the local and fused estimates of the moving average(MA) paraments and noise variance are obtained by the Gevers-Wouters algorithm and by solving linear equation by the pseudoinverse. These fused estimators have consistency.This method can be applied to signal processing with respect to speech enhancement.A simulation example shows its effectiveness.