Self-tuning weighted measurement fusion Kalman filter with cooperating identification for multisensor system with correlated noises

For the multisensor system with correlated noises and unknown noise statistics, the measurement function can be dealt with in a unified way to form a new tracking system by least square method. The result of the measurements can make some groups of steady random sequence, and the variances R_ii and covariance R_ij of these measurements can be yielded by the matrix equations of the correlation function, and then the estimates of ΓQ_wΓ^T can be obtained. Then the self-tuning weighted measurement fusion Kalman filter is obtained. A simulation example for a tracking system with 3 sensors shows its fast convergence and exactness.