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从指纹到先进的机器学习:对Wi-Fi和基于BLE的室内定位系统进行系统审查

Sara Martín-Frechina¹, Esther Dura¹, Ignacio Miralles¹

¹Department of Computer Science, ETSE, University of Valencia, Avda. de la Universidad, S/N, 46100 Burjassot, Valencia, Spain.

Sensors (Basel, Switzerland)

|November 27, 2025

概括

这篇评论探讨了使用Wi-Fi和蓝牙的室内定位系统 (IPS) 的机器学习 (ML). 它强调了深度学习 (DL) 的进步,并为未来的研究确定了环境变化等挑战.

科学领域:

计算机科学计算机科学
电气工程电气工程
无处不在的计算无处不在的计算

背景情况:

室内定位系统 (IPS) 对智能环境至关重要,从基本的信号强度发展到先进的技术.
频道状态信息 (CSI),往返时间 (RTT) 和到达角度 (AoA) 等方法越来越多地与机器学习 (ML) 集成.

研究的目的:

系统地审查基于机器学习的室内定位系统 (IPS),使用IEEE 802.11无线局域网 (Wi-Fi) 和蓝牙低能耗 (BLE).
分析测量技术,ML模型,以及从2020年到2024年在IPS文献中深度学习 (DL) 的兴起.

主要方法:

系统性文献审查遵循系统性审查和元分析 (PRISMA) 方法的首选报告项目.
分析了2020年至2024年间发表的研究,重点是Wi-Fi和基于BLE的IPS中的ML应用.

主要成果:

确定了深度学习 (DL) 方法用于室内定位的应用日益增长的趋势.
检查了最近IPS研究中使用的各种测量收集技术和ML模型.
突出了持续的实施挑战,包括环境变化,设备异质性和校准要求.

结论:

未来的研究应该专注于将ML扩展到RTT和AoA,开发混合式多度系统,并创建高效,适应性的DL模型.
无线标准和新兴技术的进步是提高下一代IPS准确性和可扩展性的关键.

关键词:

到达的角度 (AoA)蓝牙低能耗 (BLE) 技术蓝牙低能耗 (BLE)频道状态信息 (CSI) 是指通道状态信息.深度学习 (DL) 是指深度学习.在IEEE 802.11无线局域网 (Wi-Fi) 中.室内定位系统 (IPS) 是一种室内定位系统.机器学习 (ML) 是指机器学习.收到的信号强度指标 (RSSI)往返时间 (RTT)

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