Jiang Wen, Cao Ying, Yang Lin, He Zichang
School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China.
China Equipment System Engineering Company, Beijing 100039, China.
Sensors (Basel). 2017 Aug 28;17(9):1972. doi: 10.3390/s17091972.
Specific emitter identification plays an important role in contemporary military affairs. However, most of the existing specific emitter identification methods haven't taken into account the processing of uncertain information. Therefore, this paper proposes a time-space domain information fusion method based on Dempster-Shafer evidence theory, which has the ability to deal with uncertain information in the process of specific emitter identification. In this paper, radars will generate a group of evidence respectively based on the information they obtained, and our main task is to fuse the multiple groups of evidence to get a reasonable result. Within the framework of recursive centralized fusion model, the proposed method incorporates a correlation coefficient, which measures the relevance between evidence and a quantum mechanical approach, which is based on the parameters of radar itself. The simulation results of an illustrative example demonstrate that the proposed method can effectively deal with uncertain information and get a reasonable recognition result.
特定辐射源识别在当代军事事务中发挥着重要作用。然而,现有的大多数特定辐射源识别方法并未考虑不确定信息的处理。因此,本文提出了一种基于Dempster-Shafer证据理论的时空域信息融合方法,该方法能够在特定辐射源识别过程中处理不确定信息。在本文中,雷达将根据它们获得的信息分别生成一组证据,而我们的主要任务是融合多组证据以获得合理的结果。在所提出的递归集中式融合模型框架内,该方法引入了一个相关系数,用于衡量证据之间的相关性,以及一种基于雷达自身参数的量子力学方法。一个示例的仿真结果表明,所提出的方法能够有效地处理不确定信息并获得合理的识别结果。