The research of self-tuning weighted measurement fusion Kalman filter

For the linear discrete-time time-invariant system with unknown noise statistics, a new estimator is presented in this paper, that is using multisensor which has the same measurement matrix to measure, same measurements form the white noise sequences. Using the correlated functions matrix of these sequences, the measurements noise variances R_i of the subsystems can be estimated, and then the input variances Q_w can be estimated by the moving average (MA) innovation model. It is proved that the parameters estimation converges to the real parameter with probability 1. In this paper, the global optimality weighted measurement fusion Kalman filter is introduced first, and then the new estimator is presented, and finally the self-tuning weighted measurement fusion Kalman filter is shown. A simulation example for a tracking system with 3-sensor shows its effectiveness.