Abstract
The Dempster-Shafer theory (DST) is an information fusion framework and widely used in many fields. However, the uncertainty measure of a basic probability assignment (BPA) is still an open issue in DST. There are many methods to quantify the uncertainty of BPAs. However, the existing methods have some limitations. In this paper, a new total uncertainty measure from a perspective of maximum entropy requirement is proposed. The proposed method can measure both dissonance and non-specificity in BPA, which includes two components. The first component is consistent with Yager’s dissonance measure. The second component is the non-specificity measurement with different functions. We also prove the desirable properties of the proposed method. Besides, numerical examples and applications are provided to illustrate the effectiveness of the proposed total uncertainty measure.
Highlights
Measure from A Perspective ofWith the development of sensor technology, it has become a trend for complex systems to be equipped with multiple sensors
We proposed a new total uncertainty measure from the perspective of maximum entropy requirement
Compared with Deng entropy, the proposed method can effectively measure the uncertainty of basic probability assignment (BPA) when the propositions intersect with the same reliability value, and satisfy the maximum entropy property
Summary
With the development of sensor technology, it has become a trend for complex systems to be equipped with multiple sensors. Many other belief entropies have been proposed, including Höhle’s entropy [36], Yager’s dissonance measure [37], Klir and Ramer’s discord measure [38], Klir and Parviz’s strife measure [39], Jousselme’s ambiguity measure (AM) [40], Deng entropy [41], Yang and Han’s measure [42], the aggregated uncertainty measure (AU) [43], Wang and Song’s measure (SU) [44], Jirousek and Shenoy’s entropy (JS) [45], Deng’s measure [46], and so on [47,48,49] These methods can effectively measure the uncertainty of BOEs in some cases, and satisfy some desirable properties of uncertainty quantification in DST [50].
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