Self-tuning information fusion Kalman filter for the ARMA signal and its convergence

Abstract

For the multisensor autoregressive moving average (ARMA) signals with unknown model parameters and noise variances, using recursive instrumental variable (RIV) algorithm, the correlation method and the Gevers-Wouters algorithm with dead band, the information fusion estimators of model parameters and noise variances are presented. They have strong consistence. Then substituting them into the optimal fusion signal filter weighted by scalars, a self-tuning information fusion Kalman filter for the ARMA signal is presented. Further, applying the dynamic error system analysis method, it is rigorously proved that the self-tuning fused Kalman signal filter converges to the optimal fused Kalman signal filter in a realization, so that it has asymptotic optimality. A simulation example shows its effectiveness.