模糊神经网络与证据理论的飞机目标敌我识别

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 Net-works）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。

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

In order to satisfiy the complex battleidd 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 in- formation 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 recognition 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.