Unified data fusion: fuzzy logic, evidence, and rules
Ronald Mahler · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1996
In several recent papers we have demonstrated that classical single-sensor, single-source statistics can be directly extended to the multisensor, multisource case. The basis for this generalization is a special case of random set theory called 'finite-set statistics,' which allows familiar statistical techniques to be directly generalized to data fusion problems. The emphasis of previous papers has been on multisensor, multitarget detection, classification, and localization -- especially both parametric and nonparametric point estimation (MLE, MAP, and reproducing-kernel estimators). However, during the last two decades I.R. Goodman, H.T. Nguyen and others have shown that several basic aspects of expert-systems theory -- fuzzy logic, Dempster-Shafer evidential theory, and rule-based inference -- can be subsumed within a completely probabilistic framework based on random set theory. The purpose of this paper is to show that this body of research can be rigorously integrated with multisensor, multitarget estimation using random set theory as the unifying paradigm.