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Advances and challenges of AI-driven wireless indoor positioning
Comprehensive Reviews | 更新时间：2026-05-07
|
Advances and challenges of AI-driven wireless indoor positioning
- Journal on Communications Vol. 47, Issue 4, Pages: 230-247(2026)
- 作者机构：
哈尔滨工程大学计算机科学与技术学院，黑龙江 哈尔滨 150006
- 作者简介：
- 基金信息：The National Natural Science Foundation of China(62272126)
DOI：10.11959/j.issn.1000-436x.2026065
CLC： TP393Received：04 December 2025，
Revised：2026-03-01，
Accepted：02 March 2026，
Published：20 April 2026
- 稿件说明：
移动端阅览
黄心灵,冯光升,吕宏武等.AI驱动的无线室内定位的研究进展与挑战[J].通信学报,2026,47(04):230-247.
Huang Xinling,Feng Guangsheng,Lyu Hongwu,et al.Advances and challenges of AI-driven wireless indoor positioning[J].Journal on Communications,2026,47(04):230-247.
黄心灵,冯光升,吕宏武等.AI驱动的无线室内定位的研究进展与挑战[J].通信学报,2026,47(04):230-247. DOI： 10.11959/j.issn.1000-436x.2026065.
Huang Xinling,Feng Guangsheng,Lyu Hongwu,et al.Advances and challenges of AI-driven wireless indoor positioning[J].Journal on Communications,2026,47(04):230-247. DOI： 10.11959/j.issn.1000-436x.2026065.
Advances and challenges of AI-driven wireless indoor positioning
摘要
无线室内定位已深度融入交通导航、工业制造与公共安全等众多场景，是6G时代感知万物的重要基石。然而，室内环境中信号的非视距传播与多径特性严重影响定位精度，而且环境噪声与干扰也削弱其鲁棒性。人工智能的深度应用和6G通感一体化能力的持续强化，为缓解上述难题提供了新契机。基于此，提出了面向任务特性的AI室内定位技术框架，从提升定位精度、增强环境感知以及生成定位数据3类任务出发，深刻揭示由“单点位置估计”向“整体空间认知”再到“数据反向优化”的递进关系。其次，提出了面向AI定位系统的综合评价指标，重点突出AI驱动无线定位的独特特征与多维差异。最后，讨论了AI驱动的无线室内定位技术的关键挑战与发展趋势，为新一代定位技术发展提供新视角。
Abstract
Wireless indoor positioning was deeply integrated into numerous scenarios including transportation navigation
industrial manufacturing
and public safety
serving as a crucial pillar for ubiquitous sensing in the 6G era. However
positioning accuracy was severely degraded by non-line-of-sight propagation and multipath characteristics in indoor environments
while its robustness was further undermined by environmental noise and interference. As artificial intelligence was deeply applied in wireless systems and 6G’s integrated sensing and communication capabilities continue to advance
new opportunities were identified to mitigate the aforementioned challenges. A task-oriented technical framework for AI indoor positioning was established
by which a profound progression “single-point location estimation” to “holistic spatial cognition” and further to “data-driven reverse optimization” was revealed across three task categories
namely improving positioning accuracy
enhancing environmental perception and generating positioning data. Subsequently
a comprehensive set of evaluation metrics tailored specifically for AI positioning systems was proposed
which highlighted the distinctive characteristics and multidimensional variations of AI-driven wireless positioning. Finally
critical challenges and future trends in AI-driven wireless indoor positioning technology were discussed
offering fresh insights for next-generation positioning technology advancement.
关键词
Keywords
references
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