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.
Uncertainty is widely present in target recognition, and it is particularly important to express and reason the uncertainty. Based on the advantage of the evidence network in uncertainty processing, this paper presents an evidence network reasoning recognition method based on a cloud fuzzy belief. In this method, a hierarchical structure model of an evidence network is constructed; the MIC (maximum information coefficient) method is used to measure the degree of correlation between nodes and determine the existence of edges, and the belief of corresponding attributes is generated based on the cloud model. In addition, the method of information entropy is used to determine the conditional reliability table of non-root nodes, and the target recognition under uncertain conditions is realized afterwards by evidence network reasoning. The simulation results show that the proposed method can deal with the random uncertainty and cognitive uncertainty simultaneously, overcoming the problem that the traditional method has where it cannot carry out hierarchical recognition, and it can effectively use sensor information and expert knowledge to realize the deep cognition of the target intention.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.