For the multisensor multichannel ARMA signal with ARMA colored measurement noises and unknown model parameters and noise variances, this paper presents a kind of multi-stage identification method. At the first stage, the on-line information fusion estimator for the unknown model parameters is presented based on the Recursive Instrumental Variable (RIV) algorithm and the Recursive Extended Least Squares (RELS) algorithm, which is realized by computing the average of local estimators for model parameter. At the second stage, the on-line information fusion estimator for the unknown variances is obtained using the correlation method, which is realized by computing the average of the local estimators for noise variances. At the third stage, the information fusion parameter estimator of MA model is presented using the correlation method and the dead zone Gevers-Wouters and LS algorithms.

This paper puts forward an optimal and distributed fusion Kalman filter based on the Riccati equation, optimal and distributed fusion The Kalman filter has fewer calculated dimensions and less calculated amount than the centralized global optimal Kalman filter. Therefore, it has greater effect in the practice. Present the distributed local self-correcting Kalman filter at the same time. The simulation examples showed its validity.