5G高精度定位在GNSS拒绝的环境中使用定位编码增强的深度残余网络.
Jin-Man Shen1, Hua-Min Chen1, Hui Li1
1School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究介绍了一种新的深度学习模型,即定位编码多尺度剩余网络 (PE-MSRN),用于在具有挑战性的环境中精确定位5G. 与现有方法相比,PE-MSRN显著提高了准确性和速度.
科学领域:
- 无线通信是一种无线通信.
- 深度学习是一种深度学习.
- 地理空间定位是指地理空间定位.
背景情况:
- 高精度定位对于5G应用至关重要,但在没有GNSS的地区是很困难的.
- 传统方法与多路径干扰和单源数据限制作斗争.
研究的目的:
- 开发一种新的深度学习框架,以提高5G定位准确度.
- 利用5G频道状态信息 (CSI) 来改进空间信息挖掘.
主要方法:
- 提出了定位编码多尺度剩余网络 (PE-MSRN) 框架.
- 使用多粒度数据和多源5G CSI的空间采样.
- 集成位置编码 (PE) 与多尺度残余网络 (MSRN) 处理到达角度 (AOA) 数据.
主要成果:
- PE-MSRN实现了高达20厘米的定位精度.
- 与基线卷积神经网络 (CNN) 和其他算法相比,表现出优越的性能.
- 展示了更好的准确性和更快的融合,特别是在真实测量条件下.
结论:
- PE-MSRN为高可靠性5G定位系统提供了强大的解决方案.
- 该框架有效地从5G CSI中挖掘空间信息,以实现精确的定位.
- 这种方法解决了复杂环境中传统定位方法的局限性.
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