A Fusion Recognition Method Based on Temporal Evidence Reasoning

Haibin Wang, Xin Guan, Xiao Yi, Ying Liu, Guidong Sun and Dragan Poljak

Mathematical Problems in Engineering, 2023, vol. 2023, 1-14

Abstract: In order to improve the effectiveness of system decision-making, the use of the evidence theory to identify target intentions has always been a research hotspot. In information fusion using the evidence theory, there are relatively few research studies on temporal domain evidence information fusion. Due to the obvious dynamic, sequential, and real-time characteristics of temporal domain information fusion, traditional spatial domain information fusion methods are not suitable. Therefore, it is very necessary to study new methods for the temporal evidence fusion problem. In this article, a temporal evidence fusion method under the framework of the evidence reasoning rule (the ER rule) is proposed. The method uses complementary reliability integration rules and the time-series evidence distance function to obtain the reliability of evidence at adjacent moments. According to the temporal domain evidence credibility decay model, the evidence weight of the temporal domain evidence is determined. Then, through the integration of the ER rule, the temporal domain evidence reliability and evidence weight are used to combine the evidence. The capability of this method is verified by numerical experiments and compared with other methods. The results show that the proposed method can effectively deal with the temporal domain evidence combination problem, has strong anti-interference ability, and can support target intent recognition.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:5873034

DOI: 10.1155/2023/5873034

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