Self-tuning weighted measurement fusion Kalman filter

For the multisensor system with identical measurement matrix and correlated measurement noise, by the correlation method, the online estimators of the noise statistics are obtained. Based on modern time series analysis method, a self-tuning weighted measurement fusion Kalman filter is presented, which avoids Lyapunov and Riccati equations, reduces the computational burden and is suitable for real time application. By dynamic error system analysis (DESA) method, it is rigorously proved that the proposed self-tuning Kalman fuser converges to the optimal Kalman fuser with probability one or in a realization, i.e. it has asymptotical global optimality. A simulation example for a target tracking system with 3 sensors shows its effectiveness.