多维Wi-Fi接收信号强度指标数据增强基于多输出高斯过程的大规模室内定位
Zhe Tang1,2, Sihao Li1,2, Kyeong Soo Kim1
1School of Advanced Technology, Xi'an Jiaotong-Liverpool University (XJTLU), Suzhou 215123, China.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
本研究引入了多输出高斯过程 (MOGP) 来增强收到的信号强度指标 (RSSI) 数据,提高室内定位的准确性. 基于MOGP的数据增强显著提高了Wi-Fi指纹的反复神经网络 (RNN) 模型性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 室内本地化通常使用带有Wi-Fi基础设施的接收信号强度指标 (RSSI).
- 深度神经网络 (DNN) 提高了室内定位的准确性,但需要广泛的标记训练数据.
- 对DNN的数据收集是耗时且具有挑战性的,特别是在大规模环境或流行病期间.
研究的目的:
- 通过研究多维RSSI数据增强来解决室内本地化的数据短缺问题.
- 利用多输出高斯过程 (MOGP) 从多个接入点利用RSSI之间的相关性.
- 通过使用增强数据,提高最先进的室内定位模型的性能.
主要方法:
- 使用多输出高斯过程 (MOGP) 进行了多维RSSI数据增强的研究.
- MOGP被用来利用RSI在多个接入点,楼层和建筑物之间的相关性.
- 实验使用了层次性的循环神经网络 (RNN) 模型和 UJIIndoorLoc 数据库.
主要成果:
- 基于MOGP的数据增强证明了改善室内定位的可行性.
- 使用MOGP增强数据训练的RNN模型,特别是使用"单个建筑"模式,优于其他增强方法.
- 这种方法实现了8.42米的三维定位误差.
结论:
- 基于MOGP的RSSI数据增强有效地解决了室内本地化数据收集挑战.
- 通过MOGP利用RSSI之间的相关性可以提高Wi-Fi指纹的准确性.
- 拟议的方法为大型,可靠的室内定位系统提供了可行的解决方案.
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