一个新的无设备Wi-Fi室内定位,使用基于剩余注意力的卷积神经网络
Mashael Maashi1, Alanoud Al Mazroa2, Shoayee Dlaim Alotaibi3
1Department of Software Engineering, King Saud University, Riyadh, Saudi Arabia.
PeerJ. Computer science
|February 3, 2025
概括
这项研究引入了一种新的注意力增强剩余CNN (RACNN),用于使用通道状态信息 (CSI) 准确的Wi-Fi室内定位. RACNN模型显著减少了本地化错误,改善了复杂环境中的无设备跟踪.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 信号处理 信号处理
背景情况:
- 基于位置的服务 (LBS) 越来越多地用于室内定位.
- 基于Wi-Fi的室内定位使用通道状态信息 (CSI) 指纹正在获得吸引力.
- 现有的深度学习方法,如CNN,在探索CSI数据方面存在局限性,原因是受限的受收区域.
研究的目的:
- 开发一种新的本地化方法,将高精度和通用性结合起来,用于Wi-Fi室内本地化.
- 为了解决现有的CNN指纹算法中受体区域有限的问题.
- 提高在不同CSI环境中跟踪系统的通用性.
主要方法:
- 提出了注意力增强的剩余CNN (RACNN),以充分利用CSI数据和全球背景.
- 重塑了跟踪问题,将其作为一个通过深度网络解决的反任务.
- 研究并解决惯性测量单元精度偏差对跟踪性能的影响.
主要成果:
- RACNN模型实现了99.9%的定位精度.
- 将平均局部化误差降低到0.35m,比传统方法效果优于14-15%.
- 证明了实际适用性和处理复杂室内环境的能力.
结论:
- 拟议的RACNN模型在无设备Wi-Fi室内定位方面取得了重大进展.
- 注意力机制有效地捕获关键的CSI数据,以提高准确性.
- 该方法在现实室内场景中显示出高度的通用性和稳定性.
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