Abstract
There is continuously increasing interest in research on multi-sensor data fusion technology. Because Dempster’s rule of combination can be problematic when dealing with conflicting data, there are numerous issues that make data fusion a challenging task, including the exponential explosion, Zadeh Paradox, and one-vote veto. These issues lead to a great difference between the fusion results and real results. This paper applies the idea of analyzing distance-based evidence conflicts, introduces the concept of vector space, and proposes a new cosine theorem-based method of identifying and expressing conflicting data. In addition, this paper proposes a new data fusion algorithm based on the degree of mutual support between beliefs, which is based on the Jousselme distance-based combination rule proposed by Deng et al. Simulation results demonstrate that the presented algorithm achieves great improvements in both the accuracy of identifying conflicting data and that of fusing conflicting data.
Highlights
The Dempster–Shafer (DS) theory, which is known as the evidential theory, can be considered to be a generalization of the Bayesian theory
The one-vote veto issue brings about the following problem: because of the conclusion of one piece of evidence, the belief in other pieces of evidence is invalid for the final fusion result
In the Transferable Belief Model (TBM), which is proposed by Smets and Kennes [17], the value of m( ) can be larger than zero
Summary
The Dempster–Shafer (DS) theory, which is known as the evidential theory, can be considered to be a generalization of the Bayesian theory. Zadeh discovered that using Dempster’s rule of combination to deal with highly conflicting data will produce some counter-intuitive results This is known as Zadeh’s Paradox [1]. The one-vote veto issue brings about the following problem: because of the conclusion of one piece of evidence, the belief in other pieces of evidence is invalid for the final fusion result Because this problem is caused by conflicting evidence, there is a real need to identify and express highly conflicting data before using the DS theory. This paper applies a distance-based idea to deal with evidence conflict and introduces the concept of vector space On this basis, we propose a cosine theorem-based method that can effectively identify and explicitly express the conflicting data.
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