本篇論文採用經驗模態分解(EMD)法用以分析並加強5.8 GHz頻率調變連續波(FMCW)與連續波(CW)之混合式雷達系統架構所獲取之其心跳頻譜的訊雜比(SNR)。在頻域中，心跳頻率(HR)經常受呼吸頻率(RR)的高次諧波項影響，造成判讀上的誤差及錯誤。經驗模態分解出數個原始訊號之本質模態函數(IMF)，含有主要能量的本質模態函數可被選出並相加重建代表此雷達訊號的呼吸時域訊號。經消除此重建之呼吸成分，FMCW與CW模式下之心跳訊號皆可被萃取出來。實驗設計分別以健康常人與金屬板為受測物，用以驗證此混合雷達架構可偵測生理資訊與絕對距離資訊。人體實驗結果顯示心跳訊號之SNR具體提升40.74 dB與24.1 dB於CW模式與FMCW模式。萃取出的心跳時域訊號之平均心率及頻譜分析結果能達到小於1 % 以下的錯誤率。本篇研究結果顯示此混合雷達架構結合EMD方法可同時偵測距離與生理資訊。

This thesis presents an empirical model decomposition (EMD)-based method to enhance SNR of heartbeat signal obtained from a hybrid FMCW-CW 5.8 GHz ISM band radar system. EMD can solve the problem of being hindered by higher order of respiration for finding spectra of heart rate (HR). Certain intrinsic mode functions (IMFs) based on content of power ratio would be combined to rebuild respiration signal in time domain. Heartbeat signal from both FMCW and CW modes were extracted after respiration signal calibration. Experiment scenarios including target of healthy human and metal plate were carried out to detect vital sign as well as absolute range information respectively. The measurement results suggested that the extracted heartbeat signal was significantly improved by 40.74 dB from CW mode and by 24.1 dB from FMCW mode in SNR. The spectra of extracted HR indicated accuracy of heartbeat signal within error < 1 %. These findings have implications for hybrid radar system locating and acquiring physiological information simultaneously.

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