Self-tuning fusion Kalman filter for multisensor single-channel ARMA signals with coloured noises
G. L. Tao, Zili Deng · IMA Journal of Mathematical Control and Information · 2013
For the multisensor single-channel autoregressive moving average (ARMA) signals with a white measurement noise and autoregressive (AR) coloured measurement noises as common disturbance noises, 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 recursive instrumental variable (RIV) algorithm, correlation method, and the Gevers–Wouters algorithm, and the fused estimators are obtained by taking the average of the local estimators. And they have the consistency. Substituting them into the optimal information fusion Kalman filter, a self-tuning fusion Kalman filter with multi-model for single-channel ARMA signals is presented. 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.