交叉注意力变压器用于在低SNR条件下在雷达中连贯检测
Xiang Lu1,2, Zhiwen Pan3,4, Hengliang Zhou2,5
1School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
RD-Transformer是一种基于注意力的架构,增强了脉冲雷达系统中的弱目标检测. 这种新的方法显著降低了低RCS目标所需的信号噪声比 (SNR),提高了雷达性能.
科学领域:
- 雷达系统工程 雷达系统工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 在脉冲雷达中检测低RCS目标是具有挑战性的,因为传统方法需要长时间的停留时间.
- 目标运动导致迁移,使连贯积累复杂化并降低数据速率.
研究的目的:
- 提出一个端到端的基于注意力的架构,RD-Transformer,用于改进弱目标检测.
- 为了重新制定连贯集成作为一个学习的特征融合问题.
主要方法:
- 开发了RD-Transformer,这是一个集成多脉冲转位预处理和双路径自我注意编码器的架构.
- 采用交叉注意力解码器来实现发送接收交互功能,并采用基于西格形的门锁机制来控制错误报警.
主要成果:
- 与Swerling I-IV模型的最佳能量检测相比,RD-变压器将所需的SNR降低了14-20 dB.
- 在严格的错误报警约束下 (1x10^-2到1x10^-5) 仅使用10个连贯脉冲实现了这一改进.
结论:
- 通过RD转换器学习的连贯积累对于脉冲雷达中的弱目标检测是有效的.
- 拟议的方法比传统技术有了显著的进步,提高了雷达的灵敏度和数据速率.
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