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研究生： 曾遠騏
Tseng, Yuan-Chi
論文名稱： 基於 SISO/SIMO 連續波雷達系統下之都卜勒心動圖（DCG）提取演算法研究
Doppler Cardiogram Extraction Algorithms Using SISO/SIMO CW Radar Systems
指導教授： 楊慶隆
Yang, Chin-Lung
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2026
畢業學年度： 114
語文別： 英文
論文頁數： 108
中文關鍵詞： 波束成形 、弦長近似演算法 、連續波雷達 、直流偏移 、都卜勒心動圖
外文關鍵詞： beamforming, chord approximation, CW radar, direct current offset, doppler cardiogram
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    • 本研究針對自然呼吸與低訊雜比（Signal-to-noise ratio, SNR）條件下之都卜勒心動圖（Doppler Cardiogram, DCG）量測困難，提出兩套雷達架構與對應演算法的非接觸式心律監測方法，兼顧硬體可實現性與訊號解析精度。第一套系統採用低中頻連續波雷達，並提出基於弦演算法(Chord-based algorithm)的相位重建流程。該流程整合噪聲免疫運動感測技術（Noise-Immune Motion Sensing, NIMS），透過中頻訊號分段、頻譜堆疊與相位擷取以提升有效SNR；同時引入弦長近似（Chord approximation）相位解調，利用主成分分析（Principal Component Analysis, PCA）建立方向向量以判定弦段極性，進而在不依賴額外演算法校正的情況下，降低直流偏移（DC offset）與I/Q通道失衡（I/Q channel imbalance）造成的相位失真，提升心臟微小位移重建的穩健性。第二套系統進一步考量低SNR與呼吸諧波對DCG細節（如R–T區段）之遮蔽效應，採用單發多收（SIMO）連續波雷達以提升空間抑制干擾的能力，並結合波束成形與頻域數位處理以強化呼吸估測與消除，降低非心臟成分對DCG的影響，改善特徵點辨識能力。
    實驗結果顯示，本研究所提出方法能在低SNR條件下仍穩定提取與參考ECG高度對應之DCG訊號；其中低中頻雷達搭配chord-based演算法可達到平均約5.23%的R–R間隔誤差，而SIMO雷達搭配波束成形與頻域數位處理可達到平均約7.53%的R–R間隔誤差和平均約12.33%的R–T間隔誤差，由此可見本篇論文所提出系統搭配演算法的有效性。

    This study targets Doppler cardiogram (DCG) sensing under normal breathing and low signal-to-noise ratio (SNR) conditions and proposes two non-contact cardiac monitoring solutions that balance hardware feasibility and signal reconstruction accuracy. The first system adopts a low-intermediate-frequency continuous-wave (low-IF CW) radar and develops a chord-based phase reconstruction framework. It integrates Noise-Immune Motion Sensing (NIMS) to enhance effective SNR via IF-signal segmentation, spectral stacking, and phase extraction, and applies a chord-approximation demodulation in which principal component analysis (PCA) constructs a directional vector to determine chord polarity. This design mitigates phase distortion caused by DC offsets and I/Q imbalance without relying on additional calibration algorithms, improving the robustness of heartbeat micro-displacement reconstruction. To further address DCG morphology degradation (e.g., R–T segments) due to low SNR and respiratory harmonics, the second system employs a single-input multiple-output (SIMO) CW radar to strengthen spatial interference suppression and combines beamforming with frequency-domain digital processing to improve respiratory estimation and cancellation, thereby enhancing cardiac feature detectability.
    Experiments show stable DCG extraction consistent with reference ECG: the low-IF chord-based system achieves an average R–R interval error of 5.23%, while the SIMO-based system achieves 7.53% for R–R intervals and 12.33% for R–T intervals.

    摘 要 II Abstract III 誌謝 IV Table of Contents VI List of Tables IX List of Figures X List of Abbreviations XV Chapter 1 Introduction 1 1.1 Background and Objectives 1 1.2 Literature Reviews 3 1.2.1 DCG Measurement Under Breath-Holding Conditions 4 1.2.2 DCG Measurement Under Normal Breathing Conditions 6 1.2.3 Spatial-Distribution Beamforming Technique 9 1.3 Research Motivation 11 1.4 Thesis Organization 13 1.5 Research Contributions 14 Chapter 2 Fundamental Theory of CW Radar for DCG Extraction 16 2.1 Principle of Low-IF CW Radar Measurement 16 2.2 Theoretical Derivation of Phase Extraction 18 2.3 Theoretical Basis of Multi-Receiver (SIMO) System 22 Chapter 3 Proposed Methodology and Algorithm Design 25 3.1 Single Receiver Algorithm Flow 25 3.1.1 NIMS Technique 26 3.1.1.1 NIMS Technique Operation 27 3.1.1.2 SNR Enhancement Analysis 29 3.1.1.3 Selection of the Segmentation Length N 30 3.1.2 Chord Approximation 31 3.1.3 Autocorrelation Technique 33 3.2 Multi-receiver Algorithm Flow 33 3.2.1 FDDBF Technique 34 3.2.2 Respiration Removal 39 Chapter 4 Simulation Model and Algorithm Verification 41 4.1 Establishment of Physiological Signal Model 41 4.2 DCG Extraction Using a SISO Radar 43 4.2.1 Chord-based Algorithm Verification 44 4.3 DCG Extraction Using a SIMO Radar 51 4.3.1 FDDBF–CBF based Algorithm Verification 51 Chapter 5 Physiological Signal Measurement Experiments and Discussion of Results 57 5.1 Experimental Validation of DCG Extraction Using a SISO CW Radar 57 5.1.1 System Architecture of the SISO CW Radar 58 5.1.2 Selection of Radar Detection Range 61 5.1.3 Experimental Results of SISO Radar 62 5.2 Experimental Validation of DCG Extraction Using a SIMO CW Radar 69 5.2.1 Amplitude and Phase Calibration 69 5.2.2 System Architecture of the SIMO CW Radar 73 5.2.3 Experimental Results of SIMO Radar 76 5.2.4 Experimental Results of SIMO Radar on Heart Sound Experiment 80 5.3 Measurement Results and Analysis 82 Chapter 6 Conclusion and Future Work 87 6.1 Conclusion 87 6.2 Future Work 88 Reference 89

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