基于模糊神经网络的雷达对抗目标识别模型  

Target Identification Model of Radar Countermeasure Based on Fuzzy Neural Networks

作　　者：许抗 熊杰[2] 

机构地区：[1]陆军学院,南昌330103 [2]电子工程学院,合肥230037

出　　处：《舰船电子对抗》2009年第4期68-71,共4页Shipboard Electronic Countermeasure

摘　　要：在现代战场上,雷达信号密度大,体制多,波形复杂多变,而且在工作频段上往往相互重叠,在客观上对雷达对抗目标的识别造成了很大的困难。以空中雷达对抗目标的识别为例,采用过零点检测法提取雷达瞬时频率差分序列的样本方差作为脉内细微特征,结合模糊神经网络建立了一个具体的识别模型,通过仿真,证明了利用脉内细微特征和模糊神经网络技术对雷达对抗目标进行识别,是一种有效的方法。In modern battlefield, the radar signals have the characteristics of large density, various patterns, complicated and changeable waveforms, and sometimes overlap each other in operating band,which objectively brings difficulty to the target identification of radar countermeasure. Taking the target identification of air radar countermeasure as an example, this paper adopts the method of zero-crossing detection to extract the sample variance of difference sequence of radar instantaneous frequency as the in-pulse imperceptible characteristics, establishes a concrete identification model combining with the fuzzy neural network,and proves that it is a kind of effective method to identify the radar countermeasure target by the in-pulse imperceptible characteristics and fuzzy nerve network technology through the simulation.

关 键 词：雷达对抗目标识别 脉内细微特征 模糊神经网络 

分 类 号：TN971.1[电子电信—信号与信息处理]