基于压缩感知的多目标协同三维可见光定位（特邀）

刘思聪; 叶梦婉; 王先耀

doi:10.13756/j.gtxyj.2026.260010

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基于压缩感知的多目标协同三维可见光定位（特邀）

专题：面向6G的无线光通信 | 更新时间：2026-04-20

- 基于压缩感知的多目标协同三维可见光定位（特邀）
- Multi-Target Collaborative 3D Visible Light Positioning based on Compressed Sensing
- 光通信研究 2026年第2期
- 作者机构：
  
  1.厦门大学深圳研究院，广东　深圳　518057
  2.厦门大学　信息学院导航与位置服务技术国家地方联合工程研究中心，福建　厦门　361101
- 作者简介：
  
  刘思聪，副教授。E-mail：liusc@xmu.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(62471414);广东省自然科学基金资助项目(2024A1515030150);福建省自然科学基金资助项目(2023J01001);深圳市自然科学基金资助项目(JCYJ20250604123034005)
- DOI：10.13756/j.gtxyj.2026.260010
  中图分类号： TN929
- 收稿：2026-01-15，
  
  修回：2026-02-09，
  
  纸质出版：2026-04-10
- 稿件说明：
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刘思聪，叶梦婉，王先耀. 基于压缩感知的多目标协同三维可见光定位[J]. 光通信研究，2026(2): 260010.

Liu S C, Ye M W, Wang X Y. Multi-Target Collaborative 3D Visible Light Positioning based on Compressed Sensing[J]. Study on Optical Communications, 2026(2): 260010.
刘思聪，叶梦婉，王先耀. 基于压缩感知的多目标协同三维可见光定位[J]. 光通信研究，2026(2): 260010. DOI： 10.13756/j.gtxyj.2026.260010.

Liu S C, Ye M W, Wang X Y. Multi-Target Collaborative 3D Visible Light Positioning based on Compressed Sensing[J]. Study on Optical Communications, 2026(2): 260010. DOI： 10.13756/j.gtxyj.2026.260010.

摘要

目的

现有的室内可见光定位技术研究多集中于二维或固定高度的目标定位，而三维（3D）空间的多目标定位主要因空间维度的增加导致参考点数量剧增，计算与采集成本高；多目标信号混合，难以分离。针对3D室内空间中多目标难以被同步高精度定位的理论技术瓶颈，文章提出了一种融合多粒度网格与压缩感知（CS）的多目标协同3D可见光定位方法。

方法

文章所提方法的核心是设计了一种“粗定位-精细定位”两步框架：离线阶段对空间划分粗网格并聚类可见光信道指纹数据；在线阶段通过聚类匹配粗筛目标区域，然后在该区域内构建更精细的网格CS模型，通过稀疏恢复精确求解目标3D坐标。

结果

为了验证所提方法的性能，文章在典型室内房间场景下进行了仿真实验。仿真结果表明，文章所提方法在4~8个目标场景下，平均定位误差较传统方法降低了30%~50%，能够稳定实现厘米级的高精度定位。同时，该方案对目标数量的增加具有较好的鲁棒性，当目标数量增加时，其误差增长曲线较传统方法更平缓。

结论

综上所述，文章所提融合多粒度网格与CS的协同定位方法显著提升了室内3D多目标定位的精度与实用性，未来工作将考虑非视距环境及动态目标跟踪等更复杂实际环境约束，以进一步提升方法的实用性与适应性。

Abstract

Objective

Existing research on indoor visible light positioning technology predominantly focuses on two-dimensional or fixed-height target localization. The positioning of multiple targets in Three-Dimensional (3D) space faces significant challenges

primarily due to the exponential increase in the number of reference points required. It leads to high computational and data acquisition costs

and the difficulty in separating mixed signals from multiple targets. To address the theoretical and technical bottleneck of achieving simultaneous high-precision localization for multiple targets in 3D indoor spaces

this paper proposes a multi-target collaborative 3D visible light positioning method that integrates multi-granularity grids and Compressed Sensing (CS).

Methods

The core of the proposed method is a designed two-step framework of “coarse positioning followed by fine positioning”. In the offline phase

the space is partitioned using a coarse grid

and visible light channel fingerprint data is clustered. In the online phase

the target area is initially screened through cluster matching. Subsequently

a more refined grid-based CS model is constructed within this region

and the 3D coordinates of the targets are accurately solved via sparse recovery.

Results

To validate the performance of the proposed method

simulations were conducted in a typical indoor room scenario. The results demonstrate that in scenarios with 4 to 8 targets

the average positioning error is reduced by 30% to 50% compared to traditional methods

enabling stable centimeter-level high-precision positioning. Furthermore

the scheme exhibits good robustness against an increasing number of targets

as its error growth curve remains flatter than that of traditional methods when the target count rises.

Conclusion

In summary

the collaborative positioning method integrating multi-granularity grids and CS proposed in this paper significantly enhances the accuracy and practicality of indoor 3D multi-target positioning. Future work will consider more complex practical constraints such as non-line-of-sight environments and dynamic target tracking to further improve the method’s practicality and adaptability.

关键词

Keywords

references

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