认知雷达对抗中的未知雷达状态识别方法

Unknown radar state recognition method for Cognitive Radar Countermeasure

相比于传统的雷达对抗系统,认知雷达对抗引入了闭环行为学习过程,使得干扰方可以通过对雷达信号进行状态辨识,进而进行干扰效果评估,经过自主学习优化干扰策略,从而使得干扰更具有主动性和针对性。雷达状态识别是认知雷达对抗的基础,而在对抗过程中,目标雷达随时可能激活先前"隐藏"的"未知状态",这就要求干扰方能够快速对未知雷达状态做出响应。本文重点研究认知雷达对抗中的未知雷达状态识别,利用机器学习理论相关算法,提出了基于有监督分类与基于无监督聚类的2种未知状态识别方法,并通过仿真实验分别验证了2种方法的有效性。

In comparison to traditional radar countermeasure system,the closed-loop behavior learning is introduced into Cognitive Radar Countermeasure(CRCM),which conducts state recognition and jamming effect evaluation through radar signals,and then jamming strategy is optimized by autonomy making jamming more initiative and pertinent.Radar state recognition is the basis of CRCM,but the target radar may activate previously hidden unknown states in the opposed process,which compels CRCM to react to the unknown states rapidly.In consequence,unknown radar state recognition is focused on for CRCM,and two recognition methods are proposed based on supervised classification and unsupervised clustering respectively utilizing related machine learning algorithms.The simulation results validate the effectiveness of the two approaches.