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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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
- 刊出日期: 2025-01-31

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: Objective Integration of Sensing and Communication (ISAC) is an emerging technology that leverages the sharing of software and hardware resources, as well as information exchange, to integrate wireless sensing into Wi-Fi platforms, providing a cost-effective solution for indoor positioning. Existing Wi-Fi-based Channel State Information (CSI) positioning technologies are advantageous in resolving multipath signals in indoor environments, offering finer sensing granularity and higher detection accuracy. These features make them suitable for high-precision target detection and positioning in complex indoor environments, enabling the estimation of parameters such as Angle of Arrival (AoA), Time of Flight (ToF), and Doppler Frequency Shift (DFS). However, CSI-based indoor positioning faces significant challenges. On one hand, the complexity of indoor environments, including reflections from walls and pedestrian movement, reduces the Signal-to-Noise Ratio (SNR), leading to difficulties in effectively estimating signal parameters using traditional algorithms. On the other hand, indoor positioning requires high real-time performance, but most algorithms suffer from high computational complexity, resulting in low efficiency and poor real-time performance. To address these issues, this paper proposes a positioning method based on the three-dimensional (3D) Matrix Pencil (MP) algorithm, which improves the real-time performance and accuracy of existing indoor positioning parameter estimation techniques. Methods To address the real-time and accuracy issues in indoor positioning parameter estimation, a joint parameter estimation algorithm based on the 3D MP algorithm is proposed. First, the CSI data is analyzed, and Doppler parameters are integrated into the two-dimensional (2D) MP algorithm to construct a 3D matrix that includes AoA, ToF, and DFS. The 3D matrix is then smoothened, and the 3D MP algorithm is applied for parameter estimation. Clustering methods are used to obtain the AoA of the direct path, and a weighted least squares method is applied for final target position estimation, while also achieving AoA, ToF, and DFS estimation. This approach effectively improves the resolution and accuracy of parameter estimation. A two-angle positioning method is used for localization to validate the proposed algorithm. By using multiple CSI packets to construct the 3D Hankel Matrix (HM), parameter estimation accuracy is improved compared to using a single CSI packet. Compared to the 3D Multiple Signal Classification (MUSIC) algorithm, the proposed method reduces computational complexity. Incorporating the DFS parameter enhances path resolution, leading to improved AoA parameter estimation accuracy compared to the 2D MP algorithm. Results and Discussions Experiments are conducted in two different scenarios (Fig. 1), with the detailed experimental parameters provided in the table. The two scenarios tested 21 and 13 target positions, respectively. The receiver and transmitter were positioned at the same height, and their geometric relationship was confirmed using a laser rangefinder to determine positioning and direction on the ground. The results indicate that in the conference room scenario, the AoA accuracy and positioning accuracy of the 3D MP algorithm are comparable to those of the MUSIC algorithm, with the 3D MP algorithm showing a significant improvement over the 2D MP algorithm. This is because the 3D MP algorithm introduces an additional dimension to parameter estimation, improving signal resolution and making it easier to identify the direct path of the target (Fig.3). In the classroom scenario, cumulative distribution functions are used to represent overall AoA and positioning errors. For an estimation error of 0.667, the positioning accuracy of the 2D MP, MUSIC, and 3D MP algorithms are 0.73 m, 0.44 m, and 0.48 m, respectively. To observe the real-time performance, each algorithm is run ten times under identical conditions on the same computer, and the average runtime (Fig.5) is recorded. The 2D MP algorithm has the shortest runtime, while the MUSIC algorithm has the longest. The runtime of the 3D MP algorithm is approximately 90% shorter than that of the MUSIC algorithm. Conclusions This paper presents a localization method based on a 3D MP parameter estimation algorithm. A data model for the receiver is first established, and the 3D MP algorithm is introduced. Using a clustering method, the AoA of the direct path is estimated, and multiple Access Points (APs) are combined for target localization. Experimental results show that the proposed algorithm achieves an average localization accuracy of 0.56 m with an estimation error ratio of 0.667, while reducing computational complexity by 90% compared to the MUSIC algorithm. This makes the algorithm highly practical for real-time localization. The results demonstrate that the proposed method significantly reduces computational complexity while maintaining minimal positioning error when compared to the MUSIC algorithm. Although the 3D MP algorithm introduces some computational overhead compared to the 2D MP algorithm, it improves localization accuracy. Parameter estimation and localization experiments in two typical environments confirm that the proposed algorithm outperforms current systems, extending the application of Wi-Fi sensing technology within ISAC.
- Integrated Sensing and Communications (ISAC) /
- 3D Matrix Pencil (MP) /
- Angle of Arrival (AoA) /
- Time of Flight (ToF) /
- Doppler Frequency Shift (DFS)

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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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