多变量自回归信号信息融合辨识方法

近年来,为了提高系统模型和状态估计的精度,多传感器数据融合引起了广泛关注。对于带白色公共干扰噪声和有色观测噪声的多传感器多变量自回归（AR）模型,当AR模型参数和噪声方差未知时,提出了一种信息融合多段辨识方法,其中采用多维递推辅助变量（MRIV）方法得到AR模型参数的局部和融合估值器,再用相关方法得到局部和融合噪声方差估值器。这些估值器具有一致性,通过一个信号仿真例子验证了其有效性。

Information fusion identification method for multivariable autoregressive signal

In order to improve the accuracy of system model and state estimation,the data fusion of multisensor has received great attention in recent years.For the multisensor multivariable autoregressive(AR) model with white common disturbance noise and colored observation noise,when the AR model parameters and noise variances are unknown,an information fusion multi-stage identification method is presented,where the local and fused estimators of the AR model parameters are obtained by the multidimensional recursive instrumental variable(MRIV) algorithm,and the local and fused estimators of the noise variances are obtained by the correlation method.These estimators have consistency.A signal simulation example shows its effectiveness.