Self-tuning fusion state-component Kalman smoother for multisensor systems with companion form

For the single-input single-output (SISO) multisensor systems with companion form, when model parameters and noise variances are unknown, using the modern time series analysis method, based on 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 obtained. They have strong consistence. Substituting them into the optimal fusion Kalman state-component smoother, a self-tuning fusion Kalman state-component smoother is presented. Then, applying the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning Kalman fuser converges to the optimal Kalman fuser in a realization, i.e. it has asymptotic optimality. A simulation example applied to the signal processing shows its effectiveness.