一种深度神经网络多站雷达系统干扰鉴别方法

Method for discrimination of false targets in multistation radar systems based on the deep neural network

针对现有多站雷达系统欺骗式干扰鉴别方法,仅利用目标回波空间相关性单一特征作为度量标准,导致特征提取全面性不够,鉴别算法有效性和普适性不足的问题,提出一种基于深度神经网络的多站雷达系统干扰鉴别方法,将多站雷达协同探测技术在空间、时间和频率域内可用资源多、调度能力强的特点,与深度神经网络很强的模型学习和特征表示能力相结合,有效地应用于欺骗式干扰对抗领域。充分利用回波数据的未知信息,获取除相关性之外更多维、更全面、更完善、更深层的特征差异,达到更优的干扰鉴别效果。仿真实验结果证明,提出的深度神经网络干扰鉴别方法可以有效地降低噪声和脉冲数量对干扰鉴别性能的影响,缓解非理想条件下目标回波相关系数对干扰对抗技术的限制,拓宽了应用过程的边界条件。

For the existing jamming discrimination methods for multistation radar systems,only the single feature of target echo space correlation is utilized as the metric,which leads to insufficient comprehensiveness of feature extraction,so that effectiveness and universality are insufficient for the discrimination algorithm.In this paper,an identification method in multistatic radar systems based on the deep neural network is proposed.This method combines the characteristics of multistatic radar systems cooperative detection technology,which has many available resources and strong scheduling ability in space,time and frequency domain,with the strong model learning and feature representation ability in the process of information processing on the deep neural network,so that it can effectively apply to the field of anti-deception jamming.Full use is made of unknown information about echo data to obtain more multi-dimensional,more comprehensive,more complete and deeper feature differences besides correlation,so as to achieve a better jamming discrimination effect.Simulation results show that the proposed method can effectively reduce the influence of noise and pulse number on the jamming discrimination performance.At the same time,the limitation of the target echo correlation coefficient on anti-jamming technology under nonideal conditions is alleviated,which broadens the boundary conditions of the application process.