利用时间频率分布偏差和结构意识适应性用于无线通信中的宽带信号检测和识别
Xikang Wang1, Hua Xu1, Zisen Qi1
1Information and Navigation School, Air Force Engineering University of PLA, Xi'an 710077, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
本研究介绍了TFDP-SANet用于宽带信号检测和识别 (WSDR),通过利用时间频率分布先验和结构意识适应性来提高准确性. 这种新型模型增强了对信号特征的关注,并通过先进的机制优化了检测.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 宽带信号检测和识别 (WSDR) 对于频谱监测至关重要.
- 目前用于WSDR的深度学习方法经常忽略时间频率先前信息和信号结构特征.
- 优化是必要的,以提高现有的WSDR技术的准确性和稳定性.
研究的目的:
- 提出一种新的模式,TFDP-SANet,用于增强WSDR.
- 将时间频率分布先验和结构意识适应性纳入WSDR模型.
- 为了提高宽带信号的检测和识别精度.
主要方法:
- 开发了TFDP-SANet模型,其中包含SPM和Coordinate Attention (CA) 模块,用于特征提取.
- 利用适应圆高斯编码策略来生成热图,以改善中心点定位.
- 在推断过程中实施时间频率聚类优化器 (TFCO),以使用先前信息来完善边界框预测.
主要成果:
- 拟议的TFDP-SANet模型在宽带Sig53 (WBSig53) 数据集上表现出卓越的性能.
- 废弃和比较实验证实了集成模块 (SPM,CA,TFCO) 的有效性.
- 该模型显示,与现有方法相比,WSDR任务的准确性和稳定性得到了显著改善.
结论:
- 通过整合时间频率先验和结构意识,TFDP-SANet有效地解决了当前WSDR方法的局限性.
- 新型组件显著提高了模型捕获信号特征的能力,并提高了定位准确性.
- 这些发现表明了先进的宽带信号分析和识别的有希望的新方向.
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