基于分布式传感器信息融合的辐射源识别

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窄域融合,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%.

Emitter identification based on distributed sensors information fusion

Aiming at the difficult problem of basic probability assignment function(BPAF) obtaining, three methods of BPAF
obtaining based on fuzzy sets, gray association and character parameters resemble are analyzed. Mathematics expression
of obtaining BPAF is induced. Distributed sensors information fusion emitter identification model based on three BPAF
obtaining methods is constructed. Multi-periods information fusion in the time domain and distribution multi-sensors fusion
in the spatial domain are carried out in the identification process. Finally, identification rate in different signal noise ratio
(SNR) is compared with template matching method, and simulation results show that the proposed emitter identification
method is effective and the emitter accurate identification rate is above 90%.