Data fusion of multisensors estimates

Abstract

The objective of data fusion is to combine elements of raw data from different sources into a single set of meaningful information that is of greater benefit than the sum of its contributing parts. In (Yonggun Cho et al. 1996) we developed data fusion methods for a class or linear and nonlinear continuous dynamic systems with multidimensional observation vector. In this paper we present a generalization of these methods to the fusion of dependent estimates and also to discrete stochastic systems determined by difference equation. The proposed fusion methods allow fully parallel processing of information and fit in with multisensor environment. Examples demonstrate the accuracy and efficiency of the proposed fusion methods.