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Chinese Journal of Information Fusion, 2024, Volume 1, Issue 2: 134-159

Free Access | Review Article | Feature Paper | 30 September 2024
1 School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China
2 Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
3 Systems Research Institute, Polish Academy of Sciences, 00-901 Warsaw, Poland
4 National Information Processing Institute, 00-608 Warsaw, Poland
5 Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto 860-8555, Japan
* Corresponding author: Fuyuan Xiao, email: [email protected]
Received: 30 July 2024, Accepted: 28 September 2024, Published: 30 September 2024  

Abstract
Data fusion is a prevalent technique for assembling imperfect raw data coming from multiple sources to capture reliable and accurate information. Dempster–Shafer evidence theory is one of useful methodologies in the fusion of uncertain multisource information. The existing literature lacks a thorough and comprehensive review of the recent advances of Dempster– Shafer evidence theory for data fusion. Therefore, the state of the art has to be surveyed to gain insight into how Dempster–Shafer evidence theory is beneficial for data fusion and how it evolved over time. In this paper, we first provide a comprehensive review of data fusion methods based on Dempster–Shafer evidence theory and its extensions, collectively referred to as classical evidence theory, from three aspects of uncertainty modeling, fusion, and decision making. Next, we study and explore complex evidence theory for data fusion in both closed world and open world contexts that benefits from the frame of complex plane modelling. We then present classical and complex evidence theory framework-based multisource data fusion algorithms, which are applied to pattern classification to compare and demonstrate their applicabilities. The research results indicate that the complex evidence theory framework can enhance the capabilities of uncertainty modeling and reasoning by generating constructive interference through the fusion of appropriate complex basic belief assignment functions modeled by complex numbers. Through analysis and comparison, we finally propose several challenges and identify open future research directions in evidence theorybased data fusion.

Graphical Abstract
Complex Evidence Theory for Multisource Data Fusion

Keywords
multisource data fusion
Dempster--Shafer evidence theory
complex evidence theory
quantum evidence theory
uncertainty modeling
conflict management
belief function
decision making
pattern classification

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Xiao, F., Wen, J., Pedrycz, W., & Aritsugi, M. (2024). Complex Evidence Theory for Multisource Data Fusion. Chinese Journal of Information Fusion, 1(2), 134–159. https://doi.org/10.62762/CJIF.2024.999646

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