A Joint Parameter Estimation Method Based on 3D Matrix Pencil for Integration of Sensing and Communication
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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。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.
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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 ${M_p}$ 25 矩阵束参数2 $ {N_p} $ 2 矩阵束参数3 $ {B_p} $ 5 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 峰值搜索 2维MP算法 离散傅里叶变换 $ \dfrac{{11}}{4}{({M_P}{N_P})^3} + 4{({M_P}{N_P})^2}{K_M}{K_N} $ 1.28×106 奇异值分解 3维MP算法 奇异值分解 $ 11{({B_P}{M_P}{N_P})^3} + 4{({B_P}{M_P}{N_P})^2}2{K_B}{K_M}{K_N} $ 2.84×108 -
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