基于高效的跨尺度感知网络的雷达信号的脉冲内调制识别
Jingyue Liang1, Zhongtao Luo2, Renlong Liao2
1Hunan Nanoradar Science and Technology Co., Ltd., Changsha 410205, China.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
我们介绍了一个轻型卷积神经网络 (CNN),CSANet,用于雷达信号调制识别. 在低信号噪声比 (SNR) 场景中,CSANet使用新的时频融合技术实现了高精度.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 雷达信号内脉冲调制识别对于雷达系统至关重要.
- 现有的卷积神经网络 (CNN) 面临着高计算复杂性和在低信号噪声比 (SNR) 条件下性能差的挑战.
研究的目的:
- 提出一个轻量级的CNN,交叉尺度感知网络 (CSANet),以实现高效和准确的雷达信号脉冲内调制识别.
- 提高识别性能,特别是在低SNR环境中.
主要方法:
- 开发跨尺度意识 (CSA) 模块,其中包括深度扩展卷积组 (DDConv 组),跨通道交互 (CCI) 和空间信息焦点 (SIF).
- 通过使用自适应二元化,形态处理和特征融合,整合三种类型的时间频率图像 (TFI),创建一个新的时间频率融合 (TFF) 功能.
- 实施CSANet,利用TFF功能进行脉冲内调制识别.
主要成果:
- 与其他TFI相比,CSANet与拟议的TFF实现了更高的准确性.
- 在12个雷达信号数据集中,CSANet与最先进的网络相比表现出了卓越的性能.
- 拟议的方法在低SNR场景中的高精度识别中被证明有效.
结论:
- CSANet为雷达信号内脉冲调制识别提供了一种高效准确的解决方案.
- 新的TFF功能显著提高了识别性能,特别是在低SNR条件下.
- 由于CSANet的轻量级设计,它适用于需要高精度的实际雷达应用.
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