Self-tuning Measurement Fusion Kalman Predictor

For the multisensor system with unknown noise variances and with the different measurement matrices which have the same right common factor, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variance matrices are obtained. Further, a self-tuning weighted measurement fusion Kalman predictor is presented based on the Riccati equation. By using the dynamic error system analysis method, it is strictly proved that it converges to the steady state globally optimal fusion Kalman predictor in a realization or with probability one, so that it has asymptotic global optimality. A simulation example for a target tracking system with 3-sensor shows its effectiveness.