An Improved Data Fusion Method Based on Weighted Belief Entropy considering the Negation of Basic Probability Assignment

Abstract

Uncertainty in data fusion applications has received great attention. Due to the effectiveness and flexibility in handling uncertainty, Dempster–Shafer evidence theory is widely used in numerous fields of data fusion. However, Dempster–Shafer evidence theory cannot be used directly for conflicting sensor data fusion since counterintuitive results may be attained. In order to handle this issue, a new method for data fusion based on weighted belief entropy and the negation of basic probability assignment (BPA) is proposed. First, the negation of BPA is applied to represent the information in a novel view. Then, by measuring the uncertainty of the evidence, the weighted belief entropy is adopted to indicate the relative importance of evidence. Finally, the ultimate weight of each body of evidence is applied to adjust the mass function before fusing by the Dempster combination rule. The validity of the proposed method is demonstrated in accordance with an experiment on artificial data and an application on fault diagnosis.

Multi-Scale Sample Entropy-Based Energy Moment Features Applied to Fault Classification

“…The similar feature-extraction procedure can be implemented and used in same way. In addition, the entropy-based method is typical for uncertain information measuring, thus further researches can be carried out in the following areas: (i) some current tasks about entropy-based classification [31]- [33] can be expanded to solve potential conflict of uncertain information in fault diagnosis; (ii) the D-S theory can be well-used for modeling and processing of uncertain information in fault classification, for example the reliability of data source in D-S theory framework [34], and the ambiguity measure or belief entropy [35]; (iii) the fusion on conflict information is important for the future research in fault diagnosis, about which some current works [36], [37] should be paid attention to and extensively studied.…”

An Extended Base Belief Function in Dempster–Shafer Evidence Theory and Its Application in Conflict Data Fusion

“…Deng entropy can not only degenerate into Shannon entropy under certain conditions, but also gives a reasonable measurement in many complex environments [48]. Moreover, Deng entropy has been used in many practical applications, such as fault diagnosis [55], decision making [56], and sensor data fusion [34,57]. However, Deng entropy does not take into account the size of the FOD, which is also an important source of uncertain information.…”

“…BPA preprocessing can effectively eliminate the completely contradictory phenomena between evidences, which can avoid the non-intuitive fusion results caused by evidence conflict [20]. The negation of the BPA was also proposed to address uncertain information in the evidence [33,34]. The strategy in this paper is to preprocess the BPA.…”

Parameter Optimization of Ultrafine Comminution Based on Analytic Hierarchy Process: Fuzzy Comprehensive Evaluation

“…If CR ≤ 0.1, it can be acceptable. When potential conflict emerges in evaluation [21], the CR is unacceptable and the decision-maker is encouraged to repeat the pairwise comparisons, or some approaches should be adopted to process highly conflicting data [22,23].…”