Self-tuning Measurement Fusion Kalman Filter for Multisensor Systems with Companion Form and Common Disturbance Noise

For the multisensor systems with companion form and common disturbance noise, when the model parameters and noise variances are all unknown, using recursive extended least squares(RELS) algorithm, the local and fused estimators of part model parameters are obtained. And then, the information fusion noise variance estimators and other parameters estimators are presented by using correlation method and the Gevers-Wouters algorithm with a dead band. They have strong consistence. Further, a self-tuning weighted measurement fusion Kalman filter based on a self-tuning Riccati equation is presented. By the the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion Kalman filter in a realization, so that it has asymptotic global optimality. This self-tuning filter can be applied to the signal processing to obtain the self-tuning measurement fusion signal filters. A simulation example of a self-tuning fused filter for ARMA signal with 3-sensor signal shows its effectiveness.