摘要
针对多变量、环境干扰复杂的现代战场电子对抗辐射源识别非线性系统,应用早期的识别方法已经无法满足辐射源识别系统的精度要求,采用等价型模糊神经网络,产生模糊规则和隶属函数,再基于这些规则产生模糊逻辑推理来构造模式识别算法。仿真结果表明:该算法与传统模式识别方法相比具有更高的识别能力和较强的抗干扰能力,大大提高了战场辐射源识别的正确率和可靠性。
For nonlinear systems emitter identification of modern battlefield electronic warfare with multivariation and complex environ- mental interference, early identification methods have been unable to meet the accuracy requirements of emitter recognition system. We use the equivalent fuzzy neural network to generate fuzzy rules and membership functions, then based on fuzzy logic inference rules to construct a pattern recognition algorithm. Simulation results show that compared with the traditional pattern recognition meth- od, the algorithm has a higher identification ability and strong anti-jamming capability, by which the right rates and reliability of bat- tlefield emitter recognition have been greatly improved.
出处
《现代雷达》
CSCD
北大核心
2015年第1期49-51,82,共4页
Modern Radar
基金
国家自然科学基金项目(60974016)
江苏省自然科学基金项目(Bk20131097)
江苏省产学研联合创新基金项目(BY2013016)
关键词
战场辐射源
神经网络
模式识别
模糊规则
battlefield radiation
neural network
pattern recognition
fuzzy rules
