摘要
随着低空超低空突袭兵器技术的不断发展,防空导弹引信面临着越来越强的地海杂波的干扰。基于统计理论的传统信号检测很难从强地海杂波中把微弱目标信号检测出来。以相空间重构理论为基础,提出了基于神经网络的混沌时间序列建模与预测方法,探讨了相空间重构技术在引信信号检测中的应用。通过理论分析和实测杂波数据仿真,表明了该方法不需建立统计模型,适合在防空导弹引信系统中应用,能够有效地提高引信检测微弱信号的能力,为强杂波环境下的微弱信号检测提供了一个新方法。
With the continuous development of technology of low-level/hedgehopping attack weapons,air-defense missile fuze is facing more and more strong ground and sea clutter interference.It is very difficult for conventional signal detection which is based on the statistical theory to detect weak target signal from the strong clutter/sea interference.According to theory of phase-space reconstruction,a modeling and forecasting technique based on neural network for chaotic time series is proposed.The application of phase-space reconstruction technology to fuze signal detection is investigated.Theoretical analysis and real data simulation show that this method which does not need a statistical model is applicable to air-defense missile fuze system;it can effectively improve the ability of weak signal detection,providing a new method for the weak signal detection under the strong clutter environment.
出处
《探测与控制学报》
CSCD
北大核心
2009年第5期29-32,共4页
Journal of Detection & Control
关键词
相空间
重构
引信
信号检测
单步预测
phase-space
reconstruction
fuze
signal detection
signal step prodection
