Zhang Xiaozheng, Yang Chenxi, Xiao Zhengyu, Lu Binbin, Zhang Ji, Li Jianqing, Liu Chengyu
The State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China.
Chuhang Technology Co. Ltd., Nanjing, China.
Front Physiol. 2023 Jun 26;14:1206471. doi: 10.3389/fphys.2023.1206471. eCollection 2023.
Frequency-modulated continuous wave radar is capable of constant, real-time detection of human presence and monitoring of cardiopulmonary signals such as respiration and heartbeat. In highly cluttered environments or when the human body moves randomly, noise signals may be relatively large in some range bins, making it crucial to accurately select the range bin containing the target cardiopulmonary signal. In this paper, we propose a target range bin selection algorithm based on a mixed-modal information threshold. We introduce a confidence value in the frequency domain to determine the state of the human target and employ the range bin variance in the time domain to determine the range bin change status of the target. The proposed method accurately detects the state of the target and effectively selects the range bin containing the cardiopulmonary signal with a high signal-to-noise ratio. Experimental results demonstrate that the proposed method achieves better accuracy in cardiopulmonary signal rate estimation. Moreover, the proposed algorithm is lightweight in data processing and has good real-time performance.
调频连续波雷达能够持续实时检测人体存在，并监测诸如呼吸和心跳等心肺信号。在高度杂乱的环境中或人体随机移动时，某些距离单元中的噪声信号可能相对较大，因此准确选择包含目标心肺信号的距离单元至关重要。在本文中，我们提出了一种基于混合模态信息阈值的目标距离单元选择算法。我们在频域中引入置信值来确定人体目标的状态，并在时域中采用距离单元方差来确定目标的距离单元变化状态。所提出的方法能够准确检测目标状态，并有效地选择具有高信噪比的包含心肺信号的距离单元。实验结果表明，所提出的方法在心肺信号速率估计方面具有更好的准确性。此外，所提出的算法在数据处理方面轻量级，具有良好的实时性能。
