Self-tuning fusion Wiener filter for multisensor multi-channel AR signals with common disturbance noise

Abstract

For the multisensor multi-channel autoregressive (AR) signals with common disturbance noise, when model parameters and noise variances are unknown, the estimates of model parameters and noise variances can be obtained based on the multi-dimension recursive extended least squares (RELS) algorithm and the correlation method. Further, a self-tuning fusion Wiener filter is presented based on the modern time series analysis method by substituting the estimates for the true values. A simulation example shows the consistence of the estimates of the model parameters and noise variances, and the tracking characteristics of the self-tuning fusion Wiener filter.