Data fusion based state estimation of nonlinear discrete systems

We propose a geometric data fusion (GDF) method using Perception-Net which can provide error reduction, uncertainty management, and maintain consistency. We propose a Perception-Net to design a new state estimator for dynamic systems and apply the proposed geometric data fusion method to obtain the optimal estimate, propagate uncertainties and utilize the system knowledge. We present comparisons between the proposed estimator and the conventional estimators. It is also shown that the additional priori information on the system can be easily utilized in the proposed estimator to improve the performance. Through illustrative examples, it is verified that the proposed estimator presents better performances than the existing filters and improves performances via utilizing system knowledge