基于神经网络BSB模型的反辐射导弹辐射源识别技术研究

The Study on the Radiatiors Identification Technology for the Anti-Radiation Missiles Based on Neural Networks BSB Models

利用反辐射导弹来摧毁敌方雷达系统和有关设施是现代战争中一种有效的对抗手段。但是,在目前辐射源增多、信号密集的情况下,传统的信号分选和识别方式都遇到了困难,促使人们去研究以并行分布处理为特征的神经网络理论,探求一种(?)的智能信息处理方法和识别途径来解决这一难题。本文正是从这一点出发,利用BSB(脑中盒状态)模型来完成反辐射导弹的辐射源识别。计算机模拟结果证明这种方法是行之有效的。

It is an effective counter-measure means that anti-radiation missiles are used to destroy enemy's radar systems and associated equipments in modern warfare. But nowadays the radiators are ever-increasing and signals received are congesting, under this situation the traditional signal sorting and identification methods meet with difficulties, which force us to study Neural Networks with parallel-distributed processing to search for a new intelligent information processing method and path for identification to solve the problem. According to the above views, the radiators identification for the anti-radiation missiles using BSB (Box States in Brain) model are discussed in this pa-per. The results of computer simulations demonstrate that this method is effective.