Self-tuning Measurement Fusion Kalman Filter and its Convergence for Multisensor Systems with Companion Form

摘要

For the multisensor systems with companion form and unknown model parameters and noise variances, using recursive instrumental variable(RIV) algorithm, the local and fused model parameter estimators are obtained. Based on the fused model parameter estimators, the information fusion noise variance estimators are presented by using correlation method. They have strong consistence. Further, a self-tuning weighted measurement fusion Kalman filter based on a selftuning Riccati equation is presented. By 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. A simulation example applied to signal processing shows its effectiveness.