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针对辐射源识别中基本概率赋值函数(BPAF)获取的难题,提出基于模糊集、灰关联分析和特征参数相似度的3种BPAF获取法,推演了获取BPAF的数学关系,建立了基于分布式传感器数种基本概率赋值获取法的信息融合辐射源识别模型,利用该模型进行了识别实验.识别过程中进行了多周期时域融合与分布式传感器空域融合,并在不同信噪比下与模板匹配法作识别率比较.实验对比结果表明,分布式传感器信息融合识别法是有效的,辐射源平均识别率超过90%.
In order to solve the basic probability assignment function (BPAF) acquisition problem in radiation source recognition, three kinds of BPAF acquisition methods based on fuzzy sets, gray relational analysis and similarity of feature parameters are proposed, and the mathematical relationship of obtaining BPAF is deduced. A distributed sensor Several recognition models of information fusion radiation source based on the basic probabilistic assignment and acquisition method were used to identify the experiment.Multi-period temporal fusion and distributed sensor spatial fusion were performed in the recognition process and matched with the template under different SNR The comparison of the recognition rates shows that the distributed sensor information fusion recognition method is effective and the average recognition rate of radiation sources is more than 90%.