WMF Kalman predictor for the multisensor descriptor system with correlated noises

This paper develops a new state prediction algorithm for the multisensor linear stochastic descriptor system with same measurement matrix and with correlated noises. Firstly, the fused measurement is obtained based on the least square method. And the fused descriptor system is transformed to two reduced-order non-descriptor subsystem by the singular value decomposition (SVD) method. Finally, for the fused reduced-order non-descriptor subsystem, the weighted measurement fusion(WMF) Kalman predictor based on the information matrix method is presented, which can avoid solving the Riccati equation in the classical Kalman prediction method. Then, the WMF Kalman predictor and its prediction error variance for the original multisensor descriptor system are presented, according to the relationship between the original descriptor system and the reduced-order nondescriptor subsystem. The accuracy of the presented predictor is higher than that of the local predictors or state fusion predictor. A simulation example verifies the effectiveness.