Self-tuning measurement fusion Kalman filter based on the information matrix

For the multisensor systems with same measurement matrix, when the noise variances are unknown, an information fusion noise variance estimator is presented using the correlation method and least squares fusion criterion. It has the consistence and reliability of accuracy. Further, a self-tuning weighted measurement fusion Kalman filter based on the information matrix is presented. By using the dynamic error system analysis (DESA) method, based on the convergence of the self-tuning Riccati equation, it is proved that the proposed filter converges to the optimal weighted measurement fusion steady-state Kalman filter, with probability one or in a realization, so that it has the asymptotic global optimality. A simulation example for a target tracking system with 3-sensor shows its effectiveness.