通信与信息技术

针对生命体征信号中心跳与呼吸信号易受静态杂波干扰且难以分离的问题，提出一种结合压缩感知（CS）与变分模态分解（VMD）的心跳呼吸信号分离方法。该方法采用基于正交匹配追踪（OMP）的压缩感知算法对含噪生命体征信号进行稀疏重构以实现去噪，进而利用变分模态分解(VMD)算法将重构信号自适应分解为若干固有模态函数（IMF），依据呼吸与心跳的频带范围筛选对应分量，完成两种信号的有效分离。实验结果验证了所提方法在FMCW雷达生命体征信号分离中的有效性和优越性。

To address the issue that heartbeat and respiratory signals in vital‑sign signals are susceptible to static clutter interference and difficult to separate, a heartbeat‑respiration signal separation method combining Compressed Sensing(CS) and Variational Mode Decomposition(VMD) is proposed. In this method, the Compressed Sensing algorithm based on Orthogonal Matching Pursuit(OMP) is adopted to perform sparse reconstruction on noisy vital‑sign signals for denoising. Then the Variational Mode Decomposition(VMD) algorithm is used to adaptively decompose the reconstructed signals into several Intrinsic Mode Functions(IMF). Corresponding components are selected according to the frequency‑band ranges of respiration and heartbeat to realize effective separation of the two types of signals. Experimental results demonstrate the effectiveness and superiority of the proposed method for vital‑sign signal separation using FMCW radar.