基于MIMO雷达成像图序列的切向人体姿态识别方法

丁传威; 刘芷麟; 张力; 赵恒; 周庆; 洪弘; 朱晓华

doi:10.12000/JR24116

基于MIMO雷达成像图序列的切向人体姿态识别方法

DOI: 10.12000/JR24116

丁传威¹,
刘芷麟¹,
张力²,
赵恒¹,
周庆¹,
洪弘^1, ,,
朱晓华^1, ,

1.
南京理工大学南京 210000
2.
上海航天电子技术研究所上海 201109

基金项目: 国家自然科学基金(62201259, 62301255)，中央高校基本科研业务费专项资金(30923011006, 30923011026)

详细信息

作者简介:
丁传威，博士，副教授，主要研究方向为生物医学传感和雷达信号处理等

刘芷麟，硕士生，主要研究方向为基于雷达传感器的人体动作识别

张　力，博士，工程师，主要研究方向为基于雷达信号处理的杂波抑制、非接触式生命体征探测以及人体动作识别和目标识别

赵　恒，博士，讲师，主要研究方向为生物医学传感、非接触式生命体征探测以及雷达信号处理等

周　庆，博士，讲师，主要研究方向为电离层电波传播、无线电海洋遥感以及高频雷达抗干扰等

洪　弘，博士，教授，主要研究方向为生物医学传感、语音信号处理以及雷达信号处理等

朱晓华，博士，教授，主要研究方向为雷达系统、雷达信号理论以及数字信号处理等

通讯作者:
洪弘 hongnju@njust.edu.cn

朱晓华 zxh@njust.edu.cn

责任主编：金添 Corresponding Editor: JIN Tian
中图分类号: TN957.52
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出版历程
- 收稿日期: 2024-06-05
- 修回日期: 2024-07-18
- 网络出版日期: 2024-08-16

Tangential Human Posture Recognition with Sequential Images Based on MIMO Radar

1.
Nanjing University of Science and Technology, Nanjing 210000, China
2.
Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China

Funds: The National Natural Science Foundation of China (62201259, 62301255), Fundamental Research Funds for the Central Universities (30923011006, 30923011026)

More Information

Corresponding author: HONG Hong, hongnju@njust.edu.cn; ZHU Xiaohua, zxh@njust.edu.cn

摘要

摘要: 现有的基于雷达传感器的人体动作识别研究主要聚焦于相对雷达径向运动产生的微多普勒特征。当面对非径向，特别是静态姿势或者运动方向与雷达波束中心垂直的切向动作(切向人体姿态)时，传统基于微多普勒的方法无法对径向运动微弱的切向人体姿态进行有效表征，导致识别性能大幅下降。为了解决这一问题，该文提出了一种基于多发多收(MIMO)雷达成像图序列的切向人体姿态识别方法，以高质量成像图序列的形式来表征切向姿态的人体轮廓结构及其动态变化，通过提取图像内的空间特征和图序列间的时序特征，实现对切向人体姿态的准确识别。首先，通过恒虚警检测算法(CFAR)定位人体目标所在距离门，接着，利用慢时滑窗将目标动作划分为帧序列，对每帧数据用傅里叶变换和二维Capon算法估计出切向姿态的距离、俯仰角度和方位角度，得到切向姿态的成像图，将各帧成像图按照时序串联起来，构成切向人体姿态成像图序列；然后，提出了一种改进的多域联合自适应阈值去噪算法，抑制环境杂波，增强人体轮廓和结构特征，改善成像质量；最后，采用了一种基于空时注意力模块的卷积长短期记忆网络模型(ST-ConvLSTM)，利用ConvLSTM单元来学习切向人体姿态成像图序列中的多维特征，并结合空时注意力模块来强调成像图内的空间特征和图序列间的时序特征。对比实验的分析结果表明，相比于传统方法，该文所提出的方法在8种典型的切向人体姿态的识别中取得了96.9%的准确率，验证了该方法在切向人体姿态识别上的可行性和优越性。
- MIMO雷达 /
- 切向人体姿态识别 /
- 成像图序列 /
- 图像去噪 /
- 深度学习
Abstract: Recent research on radar-based human activity recognition has typically focused on activities that move toward or away from radar in radial directions. Conventional Doppler-based methods can barely describe the true characteristics of nonradial activities, especially static postures or tangential activities, resulting in a considerable decline in recognition performance. To address this issue, a method for recognizing tangential human postures based on sequential images of a Multiple-Input Multiple-Output (MIMO) radar system is proposed. A time sequence of high-quality images is achieved to describe the structure of the human body and corresponding dynamic changes, where spatial and temporal features are extracted to enhance the recognition performance. First, a Constant False Alarm Rate (CFAR) algorithm is applied to locate the human target. A sliding window along the slow time axis is then utilized to divide the received signal into sequential frames. Next, a fast Fourier transform and the 2D Capon algorithm are performed on each frame to estimate range, pitch angle, and azimuth angle information, which are fused to create a tangential posture image. They are connected to form a time sequence of tangential posture images. To improve image quality, a modified joint multidomain adaptive threshold–based denoising algorithm is applied to improve the image quality by suppressing noises and enhancing human body outline and structure. Finally, a Spatio-Temporal-Convolution Long Short Term Memory (ST-ConvLSTM) network is designed to process the sequential images. In particular, the ConvLSTM cell is used to extract continuous image features by combining convolution operation with the LSTM cell. Moreover, spatial and temporal attention modules are utilized to emphasize intraframe and interframe focus for improving recognition performance. Extensive experiments show that our proposed method can achieve an accuracy rate of 96.9% in classifying eight typical tangential human postures, demonstrating its feasibility and superiority in tangential human posture recognition.
- MIMO radar /
- Tangential human posture recognition /
- Sequential images /
- Image denoising /
- Deep learning

HTML全文

图 1 “切向弯腰”姿态的传统特征谱图

Figure 1. Conventional feature maps of the example “tangential bow” posture

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图 2 基于MIMO雷达成像图序列的切向人体姿态识别方法流程图

Figure 2. The framework of the proposed tangential human posture recognition with sequential images based on MIMO radar

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图 3 基于MIMO雷达的切向人体姿态成像图序列流程图

Figure 3. The flow chart of tangential human posture sequential images based on MIMO radar

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图 4 改进的多域联合自适应阈值人体成像图去噪算法流程图

Figure 4. The flow chart of modified joint multi-domain adaptive threshold-based image denoising algorithm

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图 5 “切向弯腰”人体姿态成像图序列

Figure 5. Sequential images of “tangential bow” human posture

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图 6 ST-ConvLSTM网络结构图

Figure 6. ST-ConvLSTM network structure diagram

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图 7 ConvLSTM单元结构图

Figure 7. The architecture of ConvLSTM cell

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图 8 空间注意力模块结构图

Figure 8. The architecture of the spatial attention module

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图 9 时间注意力模块结构图

Figure 9. The architecture of the temporal attention module

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图 10 IMAGEVK-74雷达示意图

Figure 10. IMAGEVK-74 radar schematic

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图 11 IMAGEVK-74天线示意图

Figure 11. IMAGEVK-74 antenna array

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图 12 切向人体姿态识别实验场景图

Figure 12. The experiment setup

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图 13 切向人体姿态示意图

Figure 13. Illustrations of eight typical tangential human postures

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图 14 示例“张开双臂”切向姿态去噪前后对比图

Figure 14. Images of “arm spread” posture before and after denoising processing

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图 15 基于成像图序列特征的ST-ConvLSTM网络切向人体姿态识别结果((a)—(h)分别为8种切向人体姿态)

Figure 15. Recognition results of tangential human postures by ST-ConvLSTM network based on imaging sequence ((a)—(h) indicate tangential human activities, respectively)

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图 16 切向人体姿态识别方法的t-SNE二维可视化结果

Figure 16. 2D t-SNE visualization results of tangential human postures recognition methods

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表 1 IMAGEVK-74雷达配置参数

Table 1. IMAGEVK-74 radar configuration parameters

参数	数值
起始频率	62 GHz
终止频率	66 GHz
中频带宽	100 MHz
频率步进	40 MHz
发射功率	–10 dBm
帧率	20 Hz
阵列数	20Tx, 20Rx
频率采样数	64
距离分辨率	3.75 cm
角度分辨率	6.7°

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表 2 切向人体姿态识别方法分类结果汇总

Table 2. Results of tangential human postures recognition methods

序号	输入特征	去噪算法	去噪耗时(s)	模型	模型耗时(ms)	模型尺寸(kB)	准确率(%)
方法1	时间-距离图	N/A	N/A	CNN^[40]	2.60	2602	72.7
方法2	时间-多普勒图	N/A	N/A	CNN^[40]	2.60	2602	70.3
方法3	成像图序列	N/A	N/A	ST-ConvLSTM	1.93	151	91.4
方法4	成像图序列	MTI	0.13	ST-ConvLSTM	1.93	151	89.8
方法5	成像图序列	SWT	0.03	ST-ConvLSTM	1.93	151	92.1
方法6	成像图序列	BM3D	4.40	ST-ConvLSTM	1.93	151	93.0
方法7	成像图序列	Proposed	4.61	3DCNN^[41]	2.38	3806	90.6
方法8	成像图序列	Proposed	4.61	CNN-LSTM^[42]	0.92	41299	91.4
方法9	成像图序列	Proposed	4.61	ConvLSTM	1.29	148	93.8
方法10	成像图序列	Proposed	4.61	S-ConvLSTM	1.62	150	94.5
方法11	成像图序列	Proposed	4.61	T-ConvLSTM	1.35	148	95.3
方法12	成像图序列	Proposed	4.61	ST-ConvLSTM (Proposed)	1.93	151	96.9

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表 3 面对个体差异基于留一法的所提算法鲁棒性结果

Table 3. Robustness performance in individual diversity study

志愿者	准确率(留一法)
1	93.8%
2	91.3%
3	95.0%
4	97.5%
5	96.2%
6	93.8%
7	90.0%
8	92.5%

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