电子测量与仪器学报

陈静,林同禹,阴存翊,江灏,郑绍聪.基于WiFi-CSI嗅探技术的高精度室内无人机定位方法研究[J].电子测量与仪器学报,2026,40(3):71-80

基于WiFi-CSI嗅探技术的高精度室内无人机定位方法研究

Research on high-precision indoor UAV positioning method basedon WiFi-CSI sniffing technology

DOI：

英文关键词:indoor positioning unmanned aerial vehicle channel state information deep learning 3D positioning

基金项目:福建省自然科学基金面上项目（2022J01566）、福建省高校产学合作项目（2022H6020）资助

作者	单位
陈静	1.福州大学电气工程与自动化学院福州350100；2.福州大学电力系统与产业研究院福州350100
林同禹	1.福州大学电气工程与自动化学院福州350100；2.福州大学电力系统与产业研究院福州350100
阴存翊	1.福州大学电气工程与自动化学院福州350100；2.福州大学电力系统与产业研究院福州350100
江灏	1.福州大学电气工程与自动化学院福州350100；2.福州大学电力系统与产业研究院福州350100
郑绍聪	1.福州大学电气工程与自动化学院福州350100；2.福州大学电力系统与产业研究院福州350100

Author	Institution
Chen Jing	1.School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350100, China; 2.Power Equipment System Industry Research Institution, Fuzhou University, Fuzhou 350100, China
Lin Tongyu	1.School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350100, China; 2.Power Equipment System Industry Research Institution, Fuzhou University, Fuzhou 350100, China
Yin Cunyi	1.School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350100, China; 2.Power Equipment System Industry Research Institution, Fuzhou University, Fuzhou 350100, China
Jiang Hao	1.School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350100, China; 2.Power Equipment System Industry Research Institution, Fuzhou University, Fuzhou 350100, China
Zheng Shaocong	1.School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350100, China; 2.Power Equipment System Industry Research Institution, Fuzhou University, Fuzhou 350100, China

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中文摘要:

针对现有无人机室内定位方案定位精度不足、高成本、需搭载专用定位模块的问题,设计一种基于WiFi信道状态信息（WiFi-channel state information, WiFi-CSI）嗅探技术的高精度无人机三维定位系统。该系统通过部署若干低成本的ESP32-S3传感器组成无人机感知网络,直接嗅探无人机WiFi信号并从中解析出CSI信号,结合飞行时间（time of flight, ToF）传感器获取的高度信息,实现了无需无人机搭载专用定位接收模块的轻量化高精度定位方案。针对不同高度层的CSI信号特性,进一步创新性地设计分层训练策略和稀疏神经回归网络（SE-inception-ResNE-IDCNN,SIRD）,通过融合SE注意力机制、Inception多尺度卷积与残差连接,构建CSI信号与二维坐标的映射关系,最终将预测坐标与ToF高度信息融合实现三维定位。实验结果验证了所提出系统的有效性:平均定位误差(MLE)为0.107 9 m,定位均方误差(LMSE)为0.100 3 m,定位误差的决定系数R2为0.956 2。所提出的方法较现有无人机室内定位方法精度有显著提升,为无人机室内应用提供了低成本、高精度的解决方案。

英文摘要:

To address the issues of insufficient positioning accuracy, high cost, and the requirement for dedicated onboard positioning modules in existing indoor UAV positioning schemes, this paper proposes a high-precision three-dimensional (3D) UAV positioning system based on WiFi channel state information (WiFi-CSI) sniffing. Specifically, the system deploys a number of low-cost ESP32-S3 sensors to construct a UAV sensing network, which directly sniffs the WiFi signals transmitted by the UAV and parses the CSI signals from them. Combined with the altitude information acquired by time of flight (ToF) sensors, this system achieves a lightweight and high-precision positioning solution that eliminates the need for dedicated positioning receiving modules on the UAV. Considering CSI characteristics at different altitude layers, we innovatively design a hierarchical training strategy and a sparse neural regression network (SIRD). Fusing the SE attention mechanism, Inception multi-scale convolution, and residual connections, the network establishes the mapping between CSI and 2D coordinates. 3D positioning is finally realized by fusing predicted 2D coordinates with ToF altitude. Experimental results verify the effectiveness of the proposed system are the mean localization error (MLE) is 0.107 9 m, the localization mean squared error (LMSE) is 0.100 3 m, and the coefficient of determination R2 is 0.956 2. Compared with existing indoor UAV positioning methods, the proposed method achieves a significant improvement in positioning accuracy, providing a low-cost and high-precision solution for indoor UAV applications.

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