A new data fusion method and its application to state estimation of nonlinear dynamic systems

We propose a geometric data fusion (GDF) method using a perception-net which can provide error reducing, uncertainty management, and maintaining consistency. We propose a perception-net to design a 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 existing filters and improves performances via utilizing system knowledge.