Self-tuning Information Fusion Kalman Filter for Multisensor Multi-channel ARMA Signals with Colored Measurement Noises and its Convergence

For the multisensor multi-channel autoregressive moving average (ARMA) signals with white measurement noises and an ARMA colored measurement noise as a common disturbance noise, a multi-stage information fusion identification method is presented when model parameters and noise variances are partially unknown. The local estimators of model parameters and noise variances are obtained by the multi-dimensional recursive instrumental variable (MRIV) algorithm, correlation method, and the Gevers-Wouters algorithm, and the fused estimators are obtained by taking the average of the local estimators. They have the consistency. Substituting them into the optimal fusion Kalman filter weighted by scalars, a self-tuning fusion Kalman filter for multi-channel ARMA signals is presented. It requires a less computational burden, and is suitable for real time applications. Applying the dynamic error system analysis (DESA) method, it is proved that the proposed self-tuning fusion Kalman filter converges to the optimal fusion Kalman filter in a realization, so that it has asymptotic optimality. A simulation example shows its effectiveness.