Fusion Filter Based On Kalman Filtering Method Of Multi-sensor Observation

Under the linear minimum variance optimal fused estimation criterion, by applying the Kalman filtering method, based on the Riccati equation, assuming that each sensor has the same measurement matrix, the completely functional equivalent of the centralized measurement method and the weighted measurement fusion method is proved. Based on this, the multisen-or weighting measurement fusion Kalman estimators(filter, predictor, smoother) and Wiener state estimators are presented, the weighting measurement fusion Wiener signal estimators, the weighting measurement fusion white noise deconvolution estimators and the weighting measurement fusion Wiener signal deconvolution estimators are also presented. Compared with the centralized measurement fusion method, by the weighted measurement fusion method, not only the globally optimal estimation can be obtained, but also the computational burden can obviously be reduced and it is suitable for real time applications. Many simulation examples show their effectiveness.