Abstract
For the multisensor system with unknown noise statistics and correlated measurement noises, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances and cross-covariances are obtained. Further, a self-tuning weighted measurement fusion Kalman filter is presented. Based on the stability of the dynamic error system and the concept of the convergence in a realization, it is strictly proved that the proposed self-tuning filter convergencies to the steady-state optimal Kalman filter in a realization or with probability one, so that it has asymptotic global optimality. Compared with the centralized self-tuning Kalman filter, it can reduce the computational burden, and is suitable for real time applications. A simulation example for a target tracking system with 3-sensor shows its effectiveness.
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