基于深度学习方法的LPI雷达检测与周期自相关函数的周期自相关函数
Do-Hyun Park1, Min-Wook Jeon1, Da-Min Shin1
1Department of Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
本研究引入了一种新的深度学习模型,用于检测低拦截概率 (LPI) 雷达信号. 通过利用周期自相关函数 (PACF) 和长期短期记忆网络,它提高了雷达信号检测性能.
科学领域:
- 电子战是一种电子战.
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 在电子战系统中,检测低功率,低拦截概率 (LPI) 雷达信号是一项挑战.
- 现有的统计和深度学习方法经常因为忽视固有的雷达信号特征而失败.
- 目前的方法在实现最佳雷达信号检测性能方面存在局限性.
研究的目的:
- 为LPI雷达信号开发基于深度学习的检测模型.
- 为了利用雷达信号的周期性特征来改进检测.
- 提高雷达信号检测系统的性能和效率.
主要方法:
- 利用周期自相关函数 (PACF) 在时间序列数据中捕获脉冲重复特征.
- 开发了一个深度学习模型,使用长短期记忆 (LSTM) 网络进行特征提取和检测.
- 从PACF提取了雷达信号特征,用于输入神经网络.
主要成果:
- 与使用常规自相关联或光谱输入的现有深度学习模型相比,拟议的模型表现出优异的性能.
- 由于强大的特征提取,该模型即使在浅层神经网络架构下也实现了高性能.
- 开发的模型比现有的基于深度学习的检测模型更轻,更有效.
结论:
- 深度学习模型有效地利用通过PACF的雷达信号周期性来增强LPI雷达检测.
- 提出的方法为信号检测的电子战能力提供了显著的进步.
- 该模型为具有挑战性的雷达环境提供了计算效率高和高性能解决方案.
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