AI驱动的无线室内定位的研究进展与挑战

黄心灵; 冯光升; 吕宏武; 高凯旋; 王慧强

doi:10.11959/j.issn.1000-436x.2026065

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AI驱动的无线室内定位的研究进展与挑战

综述 | 更新时间：2026-05-07

- AI驱动的无线室内定位的研究进展与挑战
- Advances and challenges of AI-driven wireless indoor positioning
- 通信学报 2026年47卷第4期页码：230-247
- 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：
  
  [ "黄心灵（2001- ），女，河南淮滨人，哈尔滨工程大学博士生，主要研究方向为无线室内定位、通感一体化等。" ]
  [ "冯光升（1980- ），男，山东禹城人，博士，哈尔滨工程大学教授、博士生导师，主要研究方向为网络智能感知、边缘智能与无线网络安全等。" ]
  [ "吕宏武（1983- ），男，山东日照人，博士，哈尔滨工程大学教授、博士生导师，主要研究方向为网络智能感知、5G高精度定位等。" ]
  [ "高凯旋（1995- ），男，北京人，博士，哈尔滨工程大学讲师，主要研究方向为室内定位、未来网络与认知网络、网络安全与信息安全等。" ]
  [ "王慧强（1960- ），男，河南周口人，博士，哈尔滨工程大学教授、博士生导师，主要研究方向为未来网络与认知网络、网络安全与信息安全、云系统与软件可信性、智慧社区与物联网等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62272126)
- DOI：10.11959/j.issn.1000-436x.2026065
  中图分类号： TP393
- 收稿：2025-12-04，
  
  修回：2026-03-01，
  
  录用：2026-03-02，
  
  纸质出版：2026-04-20
- 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.

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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