研究基于时间频域权重融合和注意力机制的干扰识别
Yao Zhang1, Shuai Wang1, Lingyi Wu1
1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
本研究引入了针对目标检测系统的新型干扰识别模型. 时间频域融合和注意力机制 (TFWF-AM) 模型在复杂的电磁环境中显著提高了准确性.
科学领域:
- 雷达系统工程 雷达系统工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 目标检测系统在准确识别干扰信号方面面临着挑战.
- 现有的干扰识别方法在能力和实际有效性方面存在局限性.
- 复杂的电磁环境会降低目标检测性能.
研究的目的:
- 开发一个先进的干扰识别模型用于目标检测.
- 克服当前干扰识别技术的缺陷.
- 为了提高目标检测在杂乱的环境中的稳定性.
主要方法:
- 为地面检测建立了一个多地形随机波动模型.
- 提出了一种时间频域加权聚变方法.
- 开发了一个干扰识别模型,集成多周期时间域,时间频域信息和注意力机制 (TFWF-AM).
主要成果:
- TFWF-AM模型在单个干扰 (99.92%) 和复合干扰 (99.56%) 中获得了最高的准确性.
- 与特征融合模型相比,观察到10.42%和52.81%的性能改进.
- 验证了结合多时期时间域和时间频率域信息的有效性.
结论:
- TFWF-AM模型为目标检测系统提供了卓越的干扰识别功能.
- 这种方法显著提高了在复杂的电磁环境中的感知和决策.
- 拟议的方法为现实世界目标检测挑战提供了强大的解决方案.
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