基于超宽带MIMO阵列成像系统的人体姿态 时频分析研究
Research on Time-Frequency Analysis of Human Postures Based on Ultra-Wideband MIMO Array Imaging System
DOI: 10.12677/csa.2026.167242, PDF,    科研立项经费支持
作者: 高天丰*, 胡 正, 刘逸璇, 陈飞宇, 于怡然, 季桓勇：中国电子科技集团公司第四十一研究所电子测试技术全国重点实验室，山东 青岛
关键词: MIMO阵列；人体姿态识别；二维时频分析；MIMO Array； Human Posture Recognition； Two-Dimensional Time-Frequency Analysis
摘要: 针对人体姿态识别面临复杂场景遮挡、运动模糊等问题，本文基于矢量网络分析技术构建了一套超宽带MIMO阵列成像系统，利用步进频连续波信号体制，对人体目标的回波数据进行时频分析处理以获取人体姿态相关信息。本文分别采用S变换、广义S变换、连续小波变换和同步挤压小波变换对实测数据进行处理并做对比分析，实测结果表明，通过本文成像系统获取的数据采用广义S变换可以更好地获取人体姿态二维时频特征，可为人体姿态识别提供良好的技术支撑。
Abstract: Aiming at challenges including occlusion in complex scenarios and motion blur existing in human posture recognition, this paper develops an ultra-wideband (UWB) MIMO array imaging system grounded on vector network analysis technology. Stepped-frequency continuous-wave signal is adopted to conduct time-frequency analysis on echo data of human targets for extracting posture-related characteristic information. Four algorithms, namely the Stockwell Transform, Generalized Stockwell Transform, Continuous Wavelet Transform and Synchrosqueezed Wavelet Transform, are separately applied to process measured experimental data for comparative research. Experimental results verify that the Generalized Stockwell Transform enables superior extraction of two-dimensional time-frequency features of human postures from data acquired by the proposed imaging system, which can deliver reliable technical support for human posture recognition.
文章引用：高天丰, 胡正, 刘逸璇, 陈飞宇, 于怡然, 季桓勇. 基于超宽带MIMO阵列成像系统的人体姿态 时频分析研究[J]. 计算机科学与应用, 2026, 16(7): 71-81. https://doi.org/10.12677/csa.2026.167242

参考文献

[1] 丁从张. 基于雷达时频谱图的人体连续行为识别方法研究[D]: [硕士学位论文]. 成都: 成都理工大学, 2022.
[2] 陈彦, 张锐, 李亚东, 等. 基于无线信号的人体姿态估计综述[J]. 雷达学报, 2025, 14(1): 229-247.
[3] 丁传威, 刘芷麟, 张力, 等. 基于MIMO雷达成像图序列的切向人体姿态识别方法[J]. 雷达学报, 2025, 14(1): 151-167.
[4] 于怡然, 韩顺利, 郝泽宇, 等. 基于步进频连续波的远程三维穿墙成像方法研究[J]. 计算机科学与应用, 2025, 15(12): 326-339.
[5] 宋永坤, 戴永鹏, 李柯蒙, 等. 基于云时空特征的超宽带雷达轻量化人体行为识别方法[J]. 雷达学报, 2026, 15(1): 78-90.
[6] 金添, 宋永平, 胡锡坤, 等. 基于4D成像雷达的隔墙人体姿态重建与行为识别研究[J]. 雷达学报, 2026, 15(1): 91-105.
[7] Kim, Y. and Lee, J. (2016) Human Motion Classification Based on Micro-Doppler Time-Frequency Signatures Using Support Vector Machines. IEEE Sensors Journal, 16, 4890-4898.
[8] Shrestha, S. and Wang, L. (2023) Wavelet Time-Frequency Analysis for FMCW Radar Human Activity Recognition under Partial Occlusion. Remote Sensing, 15, Article 2145.
[9] Lai, G.J., Lou, X. and Ye, W.B. (2021) Radar-Based Human Activity Recognition with 1-D Dense Attention Network. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. [Google Scholar] [CrossRef] 
[10] 张岩. 基于雷视融合的多域特征人体行为识别研究[D]: [硕士学位论文]. 哈尔滨: 哈尔滨工业大学, 2025.
[11] 薛宝泽. 基于毫米波与深度学习的人体姿态识别[D]: [硕士学位论文]. 西安: 西安电子科技大学, 2023.
[12] Gilles, J. (2013) Empirical Wavelet Transform. IEEE Transactions on Signal Processing, 61, 3999-4010. [Google Scholar] [CrossRef] 
[13] Cho, H. and Park, Y. (2025) UWB Radar-Based Human Activity Recognition via EWT-Hilbert Spectral Videos and Dual-Path Deep Learning. Electronics, 14, Article 3264. [Google Scholar] [CrossRef] 
[14] 屈乐乐, 张丁元, 杨天虹, 等. 基于双流特征融合的FMCW雷达人体连续动作识别[J]. 雷达科学与技术, 2022, 20(5): 621-627.
[15] Noon, D.A. (1996) Stepped-Frequency Radar Design and Signal Processing Enhances Ground Penetrating Radar Performance. The University of Queensland.
[16] 曹凌霄. 穿墙透视雷达三维透视成像技术研究[D]: [硕士学位论文]. 成都: 电子科技大学, 2019.
[17] Stockwell, R.G., Mansinha, L. and Lowe, R.P. (1996) Localization of the Complex Spectrum: The S Transform. IEEE Transactions on Signal Processing, 44, 998-1001. [Google Scholar] [CrossRef] 
[18] 陈学华. 时频分布与地震信号谱分析研究[D]: [硕士学位论文]. 成都: 成都理工大学, 2006.
[19] Daubechies, I., Lu, J. and Wu, H.T. (2011) Synchrosqueezed Wavelet Transforms: An Empirical Mode Decomposition-Like Tool. Applied and Computational Harmonic Analysis, 30, 243-261. [Google Scholar] [CrossRef] 
[20] 贺梅. 基于广义S变换的时频分析方法及其应用[D]: [硕士学位论文]. 长春: 吉林大学, 2015.