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

YANG Xiaolong; ZHANG Bingrui; ZHOU Mu; ZHANG Wen

doi:10.11999/JEIT240003

Volume 47 Issue 1

Jan. 2025

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YANG Xiaolong, ZHANG Bingrui, ZHOU Mu, ZHANG Wen. A Joint Parameter Estimation Method Based on 3D Matrix Pencil for Integration of Sensing and Communication[J]. Journal of Electronics & Information Technology, 2025, 47(1): 84-92. doi: 10.11999/JEIT240003

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YANG Xiaolong, ZHANG Bingrui, ZHOU Mu, ZHANG Wen. A Joint Parameter Estimation Method Based on 3D Matrix Pencil for Integration of Sensing and Communication[J]. Journal of Electronics & Information Technology, 2025, 47(1): 84-92. doi: 10.11999/JEIT240003

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

doi: 10.11999/JEIT240003

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)

Received Date: 2024-01-16
Rev Recd Date: 2024-07-03

Available Online: 2024-08-02

Publish Date: 2025-01-31

Abstract

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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References(31)

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