J4 ›› 2012, Vol. 30 ›› Issue (1): 78-82.

• 论文 • 上一篇    下一篇

模糊神经网络与证据理论的飞机目标敌我识别

李勇|王德功|杨佐龙   

  1. 空军航空大学 航空电子工程系,长春 |130022
  • 收稿日期:2011-11-02 出版日期:2012-01-29 发布日期:2012-03-20
  • 作者简介:李勇(1988—)|男|江西萍乡人|空军航空大学硕士研究生,主要从事模式识别与信息处理研究,(Tel)86-431-86034364(E-mail)594199475@qq.com;王德功(1955—)|男|江苏徐州人|空军航空大学教授|硕士生导师,主要从事雷达目标识别研究,(Tel)86-431-86959066(E-mail)2495202664@qq.com。

Fuzzy Neural Networks and D-S Theory Used in Friend and Foe Identification of |Aircraft Target

LI Yong,WANG De-gong,YANG Zuo-long   

  1. Department of Aviation Electronic Engineering,Aviation University of Airforce,Changchun |130022,China
  • Received:2011-11-02 Online:2012-01-29 Published:2012-03-20

摘要:

为满足复杂环境下目标敌我属性识别能力,提出了一种基于模糊神经网络(FNN:Fuzzy Neural Networks)和证据理论的新敌我识别方法。该方法利用模糊神经网络和证据理
论信息的处理能力,将敌我识别器(IFF:Identification Friend -or-Foe)、电子支援措施(ESM:Electronic Warfare Support Measure)、雷达及红外获取的信息融合,进行敌我识别。
仿真结果表明,该方法的识别能力明显优于单一模糊神经网络分类器,识别率达0.994,同时具有很强的容错性和一定的抗干扰能力,更适合战场需要。

关键词: 模糊神经网络, 证据理论, 数据融合, 敌我识别

Abstract:

In order to satisfiy the complex battlefield environment,we raise a new method to realize friend and foe identification.Using the ablity of fuzzy neural networks and D-S theory in information processing,fusing the information acquired from IFF(Identification Friend-or-Foe),ESM(Electronic Warfare Support Measure),radar and infrared,the identification is realized.The simulation results show that the recognition ability of the method is superior to a single FNN (Fuzzy Neural Networks),classifier r
ecognition rate is 0.978.And it  also has a strong fault-tolerance and a certain degree of immunity,it better suited to the battlefield needs.

Key words: fuzzy neural networks, D-S theory;data fusion, friend and foe identification

中图分类号: 

  • TP319.4