面向通感一体化的三维矩阵束联合参数估计算法

杨小龙; 张冰睿; 周牧; 张文

doi:10.11999/JEIT240003

面向通感一体化的三维矩阵束联合参数估计算法

doi: 10.11999/JEIT240003

重庆邮电大学通信与信息工程学院重庆 400065

基金项目: 国家自然科学基金(62101085)，重庆市九龙坡区科技计划项目(2022-02-005-Z)，重庆市研究生科研创新项目(CYS23457)

详细信息

作者简介:
杨小龙：男，副教授，硕士生导师，研究方向为通感一体化、无线定位与感知

张冰睿：女，硕士生，研究方向为无线定位与感知

周牧：男，教授，博士生导师，研究方向为无线定位

张文：男，硕士，研究方向为无线定位与感知

通讯作者:
周牧　zhoumu@cqupt.edu.cn

中图分类号: TN929.5
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- 文章访问数: 208
- HTML全文浏览量: 66
- PDF下载量: 17
- 被引次数: 0
出版历程
- 收稿日期: 2024-01-16
- 修回日期: 2024-07-03
- 网络出版日期: 2024-08-02

A Joint Parameter Estimation Method Based on 3D Matrix Pencil for Integration of Sensing and Communication

School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62101085), The Science and Technology Research Project of Chongqing Jiulongpo District (2022-02-005-Z), Chongqing Graduate Student Research Innovation Project (CYS23457)

摘要

摘要: 作为一种基于软硬件资源共享和信息共享的新型信息通信技术，通感一体化(ISAC)可将无线感知集成到Wi-Fi平台，为低成本的室内定位提供一种高效的方法。针对室内定位参数估计实时性与准确性问题，该文提出一种基于3维矩阵束(MP)联合参数估计算法。首先，对信道状态信息(CSI)数据进行分析，构建包含到达角(AoA)、飞行时间(ToF)和多普勒频移(DFS)的3维矩阵。其次，对3维矩阵进行平滑处理并利用3维MP算法进行参数估计，通过聚类找到直达径。最后，利用双角定位法进行定位，验证该文所提算法的有效性。实验结果表明，与多重信号分类(MUSIC)参数估计算法相比，无需复杂的峰值搜索步骤，降低了90%计算复杂度。与2维MP算法相比，加入多普勒参数，使AoA估计误差均值在会议室和教室两种场景下分别降低了1.45°和2°。该文通过实际测试验证了所提算法在室内可以达到在置信度67%处平均0.56 m的定位精度。因此，该文所提算法有效地改善了现有室内定位参数估计的实时性和准确性。
- 通感一体化 /
- 3维矩阵束 /
- 到达角 /
- 飞行时间 /
- 多普勒频移
Abstract: As a new information communication technology based on software and hardware resource sharing and information sharing, Integration of Sensing and Communication (ISAC) can integrate wireless sensing into Wi-Fi platforms, providing an efficient method for low-cost indoor localization. Focusing on the problem of real-time and accuracy of indoor positioning parameter estimation, a joint parameter estimation algorithm based on three Dimensional (3D) Matrix Pencil (MP) is proposed. First, the Channel State Information (CSI) data is analyzed and a 3D matrix containing Angle of Arrival (AoA), Time of Flight (ToF), and Doppler Frequency Shift (DFS) is constructed. Secondly, the 3D matrix is smoothed and the 3D MP algorithm is used for parameter estimation, the direct path is found by clustering. Finally, the triangulation method is used for positioning to verify the effectiveness of the proposed algorithm. Experimental results show that compared with the MUltiple SIgnal Classification (MUSIC) parameter estimation algorithm, there is no need for complicated peak search steps, and the computational complexity is reduced by 90%. Compared with the two-dimensional MP algorithm, adding DFS can effectively improve the resolution and accuracy of parameter estimation. The actual test verifies that the proposed algorithm can achieve an average positioning accuracy of 0.56 m at a confidence level of 67% indoors. Therefore, the proposed algorithm effectively improves the real-time and accuracy of the existing indoor positioning parameter estimation.
- Integrated Sensing and Communications (ISAC) /
- 3D Matrix Pencil (MP) /
- Angle of Arrival (AoA) /
- Time of Flight (ToF) /
- Doppler Frequency Shift (DFS)

HTML全文

图 1 实验场景图

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图 2 实验平面结构图

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图 3 会议室场景误差分析图

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图 4 教室场景误差分析图

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图 5 不同算法的运行时间

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表 1 实验参数

参数名称	符号	数值
接收天线数量	$N$	4
子载波数量	$M$	49
包的数量	$B$	10
矩阵束参数1	${M_{\rm p}}$	25
矩阵束参数2	$ {N_{\rm p}} $	2
矩阵束参数3	$ {B_{\rm p}} $	5

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表 2 实验参数

算法	主要步骤	算法复杂度	参考数值
MUSIC算法	特征值分解	$ \begin{gathered} \left\{ {{{(BMN)}^2}\left( {BMN - q} \right) + {{(BMN - q)}^2}BMN + {{(BMN)}^2}} \right\} \\ \times {\mathrm{sr}}\_{\mathrm{AoA}} \times {\mathrm{sr}}\_{\mathrm{ToF}} \times {\mathrm{sr}}\_{\mathrm{DFS}} \\ \end{gathered} $	1.25×10¹⁶
MUSIC算法	峰值搜索		1.25×10¹⁶
2维MP算法	离散傅里叶变换	$ \dfrac{{11}}{4}{({M_{\rm p}}{N_{\rm p}})^3} + 4{({M_{\rm p}}{N_{\rm p}})^2}{K_M}{K_N} $	1.28×10⁶
2维MP算法	奇异值分解		1.28×10⁶
3维MP算法	奇异值分解	$ 11{({B_{\rm p}}{M_{\rm p}}{N_{\rm p}})^3} + 4{({B_{\rm p}}{M_{\rm p}}{N_{\rm p}})^2}2{K_B}{K_M}{K_N} $	2.84×10⁸

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