Conflicting Information Fusion Based on an Improved DS Combination Method

Abstract

An effective and reliable fusion method for conflicting information is proposed in this paper. Compared with a single-sensor system, a multi-sensor fusion system can comprehensively combine the redundancy and complementarity of multi-sensor information to obtain better system performance. Hence, the multi-sensor fusion system has become one of the research hotspots. However, due to lack knowledge about the measurement environment and limited sensor accuracy, the multi-sensor system inevitably appears to have imperfect, uncertain and inconsistent information. To solve the problem, we introduce one powerful uncertainty reasoning method: Dempster-Shafer theory (DS theory). With convincing measurement and a forceful combination of uncertain information, DS theory is widely applied in various fields, like decision-making, expert systems, target tracking, monitoring systems, etc. Nevertheless, DS theory will produce counter-intuitive fusion results when the pieces of evidence are highly conflicting. To address this issue, we raise an improved DS combination method for conflicting information fusion in this paper. First of all, the modified Minkowski distance function and the betting-commitment distance function are separately employed to revise potentially conflicting pieces of evidence. The procedure availably solves the conflicting situations caused by unreliable and imprecise evidence sources, which enhances the consistency among pieces of evidence. Then, based on two revised pieces of evidence, a conflicting redistribution strategy based on locally conflicting analyses is put forward. The approach dexterously combines two revised pieces of evidence to avoid conflicting situations caused by compulsive normalization, which further improves the accuracy and convergence speed of the multi-sensor fusion system. Finally, two experimental analyses with consistent information and conflicting information reveal the remarkable effectiveness and priority of the proposed algorithm for the multi-sensor fusion system. Consequently, this paper has certain value for the multi-sensor fusion system.

A Comparative Study of Bayesian and Dempster-Shafer Fusion on Image Forgery Detection

“…As a topic for further research, we shall devote to analyzing more deeply on each fusion method. Particularly, the limitation of the traditional DST fusion when dealing with conflict evidence shall be studied further [65]. Moreover, we shall consider more advanced combination rules in DST fusion such as the transferable belief model (TBM) [66] and Dezert-Smarandache theory (DSmT) [67].…”

Combined Conflict Evidence Based on Two-Tuple IOWA Operators

“…The Dempster-Shafer (D-S) theory of evidence [1][2][3][4][5][6][7] was introduced by Dempster and Shafer [8]. It can efficiently cope with imprecise and uncertain information without transcendental knowledge.…”