基于对比学习的半监督雷达工作模式识别
Peishan Sun1, Mingyang Du1, Zhihui Li1
1College of Electronic Countermeasure, National University of Defense Technology, Hefei 230071, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
这项研究引入了一种新的半监督深度学习框架,用于雷达模式识别,大大减少了对标记数据的需求. 这种新的方法使用最小的标记样本实现了最先进的准确性.
科学领域:
- 人工智能的人工智能
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 细粒度雷达模式识别对于各种应用至关重要.
- 当前的深度学习模型需要大量的标记数据,而这些数据的获取是昂贵且耗时的.
- 雷达模式识别的一个重大瓶是严重依赖昂贵的标记数据.
研究的目的:
- 为细粒度雷达模式识别开发一种新的半监督框架.
- 为了有效地利用未标记的数据来克服数据稀缺问题.
- 为了在雷达模式识别中以最小的标记数据实现高精度.
主要方法:
- 设计了一个端到端的,三分支的框架.
- 整合了一种双重对比学习机制.
- 纳入了针对脉冲扭曲的定制策略.
主要成果:
- 拟议的框架大大提高了精度17%至34%使用仅10%的标记数据.
- 该模型在两个具有挑战性的数据集上实现了最先进的性能.
- 用最小的标记数据获得了高精度,证明了框架的有效性.
结论:
- 半监督学习是解决雷达模式识别数据稀缺问题的可行方法.
- 拟议的框架为细粒度雷达模式识别提供了高效和有效的解决方案.
- 这项工作在雷达模式识别中建立了一个新的最先进的技术,减少了标记工作.
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