面向通感一體化的三維矩陣束聯(lián)合參數(shù)估計算法

楊小龍; 張冰睿; 周牧; 張文

doi:10.11999/JEIT240003

面向通感一體化的三維矩陣束聯(lián)合參數(shù)估計算法

doi: 10.11999/JEIT240003

重慶郵電大學(xué)通信與信息工程學(xué)院重慶 400065

基金項目: 國家自然科學(xué)基金(62101085)，重慶市九龍坡區(qū)科技計劃項目(2022-02-005-Z)，重慶市研究生科研創(chuàng)新項目(CYS23457)

詳細信息

作者簡介:
楊小龍：男，副教授，碩士生導(dǎo)師，研究方向為通感一體化、無線定位與感知

張冰睿：女，碩士生，研究方向為無線定位與感知

周牧：男，教授，博士生導(dǎo)師，研究方向為無線定位

張文：男，碩士，研究方向為無線定位與感知

通訊作者:
周牧　zhoumu@cqupt.edu.cn

中圖分類號: TN929.5
計量
- 文章訪問數(shù): 316
- HTML全文瀏覽量: 100
- PDF下載量: 50
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-01-16
- 修回日期: 2024-07-03
- 網(wǎng)絡(luò)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: 作為一種基于軟硬件資源共享和信息共享的新型信息通信技術(shù)，通感一體化(ISAC)可將無線感知集成到Wi-Fi平臺，為低成本的室內(nèi)定位提供一種高效的方法。針對室內(nèi)定位參數(shù)估計實時性與準確性問題，該文提出一種基于3維矩陣束(MP)聯(lián)合參數(shù)估計算法。首先，對信道狀態(tài)信息(CSI)數(shù)據(jù)進行分析，構(gòu)建包含到達角(AoA)、飛行時間(ToF)和多普勒頻移(DFS)的3維矩陣。其次，對3維矩陣進行平滑處理并利用3維MP算法進行參數(shù)估計，通過聚類找到直達徑。最后，利用雙角定位法進行定位，驗證該文所提算法的有效性。實驗結(jié)果表明，與多重信號分類(MUSIC)參數(shù)估計算法相比，無需復(fù)雜的峰值搜索步驟，降低了90%計算復(fù)雜度。與2維MP算法相比，加入多普勒參數(shù)，使AoA估計誤差均值在會議室和教室兩種場景下分別降低了1.45°和2°。該文通過實際測試驗證了所提算法在室內(nèi)可以達到在置信度67%處平均0.56 m的定位精度。因此，該文所提算法有效地改善了現(xiàn)有室內(nèi)定位參數(shù)估計的實時性和準確性。
- 通感一體化 /
- 3維矩陣束 /
- 到達角 /
- 飛行時間 /
- 多普勒頻移
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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圖 1 實驗場景圖

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圖 2 實驗平面結(jié)構(gòu)圖

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圖 3 會議室場景誤差分析圖

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圖 4 教室場景誤差分析圖

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圖 5 不同算法的運行時間

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表 1 實驗參數(shù)

參數(shù)名稱	符號	數(shù)值
接收天線數(shù)量	$N$	4
子載波數(shù)量	$M$	49
包的數(shù)量	$B$	10
矩陣束參數(shù)1	${M_{\rm p}}$	25
矩陣束參數(shù)2	$ {N_{\rm p}} $	2
矩陣束參數(shù)3	$ {B_{\rm p}} $	5

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表 2 實驗參數(shù)

算法	主要步驟	算法復(fù)雜度	參考數(shù)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維MP算法	離散傅里葉變換	$ \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維MP算法	奇異值分解		1.28×10⁶
3維MP算法	奇異值分解	$ 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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