基于卷积神经网络-通道注意模块的无视线/视线识别方法的研究
Jingjing Zhang1,2,3, Qingwu Yi1,2,3, Lu Huang1,2
1State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
本研究引入了一种使用带有频道注意模块 (CNN-CAM) 的卷积神经网络来准确识别视线 (LOS) 和非视线 (NLOS) UWB信号的新方法,提高定位可靠性.
科学领域:
- 无线通信无线通信
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 非视线 (NLOS) 传播会降低超宽带 (UWB) 定位精度.
- 准确识别视线 (LOS) 和NLOS条件对于可靠的UWB定位至关重要.
- 现有的基于频道脉冲响应 (CIR) 的NLOS/LOS识别方法缺乏准确性和通用性.
研究的目的:
- 为UWB信号开发一种先进的NLOS/LOS识别方法.
- 提高UWB定位系统的准确性和可靠性.
- 用深度学习来解决当前识别技术的局限性.
主要方法:
- 提出了一个多层卷积神经网络 (CNN),集成与道注意模块 (CAM).
- 利用了原来的频道脉冲响应 (CIR) 时间域特征.
- 采用全球平均聚合功能集成和分类,取代传统的完全连接层.
- 使用公开的eWINE数据集验证了模型.
主要成果:
- 拟议的CNN-CAM模型实现了92.29%的LOS召回和87.71%的NLOS召回.
- 总体准确率达到90.00%,F1得分为90.22%.
- 与现有的先进识别方法相比,证明了卓越的性能.
结论:
- CNN-CAM模型有效地识别了UWB信号中的LOS和NLOS条件.
- 这种方法可以显著提高定位准确性,因为它可以更好地处理测距结果.
- 拟议的方法为UWB本地化挑战提供了一个强大的和可通用的解决方案.
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