本論文專注於提高頻率解析度，以改善使用短時窗長時的多人生理感測。所提出的基於小波和餘弦轉換超解析度演算法(wavelet- and cosine-transform-based super-resolution algorithm, WCT-SRA)透過使用基於餘弦轉換的方法估計訊號的振幅、頻率和初始相位，顯著提高了頻率解析度。此外，小波分解使每位受測者成分在分解的過程更有效率。兩個致動器的實驗結果顯示WCT-SRA能夠區分兩個振動間隔頻率極為接近的金屬板，其僅在時間窗長5秒、10秒、和20秒，便能夠分辨分別為0.1 Hz、0.05 Hz、和0.01 Hz的頻率解析度。除此之外，透過調整受測者與雷達間的距離，在20秒的時間窗長同時辨別四位個體的生理訊號，而單人僅需3秒的時間窗長。

This thesis focuses on enhancing the frequency resolution to improve the multi-person vital sign detection using a short time window. The proposed wavelet- and cosine-transform-based super-resolution algorithm (WCT-SRA) significantly improves the frequency resolution by estimating the amplitude, frequency, and initial phase of a signal using a cosine-transform-based method. Moreover, wavelet decomposition makes the decomposition process for each subject’s component more efficient. Experimental results with two actuators demonstrate that the WCT-SRA can distinguish closely spaced vibration frequencies of two metal plates and enable frequency resolutions of 0.1 Hz, 0.05 Hz, and 0.01 Hz with time windows of only 5 s, 10 s, and 20 s, respectively. Moreover, by adjusting the initial distances of subjects from the radar, the vital signs of four individuals were simultaneously determined during a 20-s time window, and a single subject for only a 3-s time window.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
目錄 v
圖次 vii
表次 x
第一章 序論 1
1.1 研究背景 1
1.2 雷達系統介紹 2
1.2.1 連續波雷達 3
1.2.2 自我注入鎖定雷達 3
1.2.3 相位與自我注入鎖定雷達 4
1.3 多人感測技術 5
1.3.1 超材料洩漏波天線 6
1.3.2 RELAX、CLEAN演算法 6
1.3.3 多訊號分類演算法 7
1.3.4 頻率估計演算法 8
1.3.5 餘弦轉換、小波轉換 9
1.4 章節規劃 14
第二章 系統架構與感測原理 15
2.1 相位與正交自我注入鎖定雷達 15
2.2 雷達系統架構 18
2.3 單與多人感測原理 19
2.3.1 單人感測原理 20
2.3.2 雙人感測原理 21
2.3.3 三人感測原理 22
2.3.4 四人感測原理 25
第三章 演算法與訊號處理 29
3.1 基於餘弦轉換的振幅、頻率、相位校正技術 29
3.2 基於小波和餘弦轉換超解析度演算法 33
3.3 演算法頻率解析度極限模擬 35
3.4 單與多訊號感測模擬 40
3.4.1 單訊號感測模擬 40
3.4.2 雙訊號感測模擬 42
3.4.3 三訊號感測模擬 43
3.4.4 四訊號感測模擬 45
第四章 多人生理訊號感測實驗 48
4.1 演算法頻率解析度極限實驗 48
4.2 單人與多人生理訊號感測實驗 52
4.2.1 單人生理訊號感測實驗 53
4.2.2 雙人生理訊號感測實驗 55
4.2.3 三人生理訊號感測實驗 56
4.2.4 四人生理訊號感測實驗 58
4.3 系統及演算法的限制討論 61
第五章 結論 67
參考文獻 68

[1] 中華民國人口推估(2022年至2070年), 國家發展委員會 [Online] Available:
https://ppws.ndc.gov.tw/Download.ashx?u=LzAwMS9VcGxvYWQvNDY0L3JlbGZpbGUvMTAzNDcvNTAvMTMxNmIxMGYtMzUzYS00NDk3LTk2N2YtN2M2MjA5ZjIwNzZmLnBkZg%3d%3d&n=5Lit6I%2bv5rCR5ZyL5Lq65Y%2bj5o6o5LywKDIwMjLlubToh7MyMDcw5bm0KeWgseWRii5wZGY%3d&icon=.pdf
[2] 多項生理睡眠檢查, Wikimedia Foundation Inc. [Online] Available: https://zh.wikipedia.org/zh-tw/多項生理睡眠檢查
[3] F.-K. Wang et al., “A novel vital-sign sensor based on a self-injection-locked oscillator,” IEEE Trans. Microw. Theory Techn., vol. 58, no. 12, pp. 4112–4120, Dec. 2010.
[4] F.-K. Wang, T.-S. Horng, K.-C. Peng, J.-K. Jau, J.-Y. Li, and C.-C. Chen, “Single-antenna Doppler radars using self and mutual injection locking for vital sign detection with random body movement cancellation,” IEEE Trans. Microw. Theory Techn., vol. 59, no. 12, pp. 3577–3587, Dec. 2011.
[5] F.-K. Wang, C.-H. Fang, T.-S. Horng, K.-C. Peng, J.-Y. Li, and C.-C. Chen, “Concurrent vital sign and position sensing of multiple individuals using self-injection-locked tags and injection-locked I/Q receivers with arctangent demodulation,” IEEE Trans. Microw. Theory Techn., vol. 61, no. 12, pp. 4689–4699, Dec. 2013.
[6] F.-K. Wang, P.-H. Juan, S.-C. Su, M.-C. Tang, and T.-S. Horng, “Monitoring displacement by a quadrature self-injection-locked radar with measurement- and differential-based offset calibration methods,” IEEE Sensors J., vol. 19, no. 5, pp. 1905–1916, Mar. 2019.
[7] F. Adib, H. Mao, Z. Kabelac, D. Katabi, and R. C. Miller, “Smart homes that monitor breathing and heart rate,” in Proc. 33 rd Annu. ACM Conf. Hum. Factors Comput. Syst., Seoul, Republic of Korea, Apr. 18-23 2015, pp. 837–846.
[8] S. Yue, H. He, H.Wang, H. Rahul, and D. Katabi, “Extracting multiperson respiration from entangled RF signals,” in Proc. ACM Interactive, Mobile, Wearable Ubiquitous Technol. (Ubicomp), vol. 2, no. 2, Jul. 2018, pp. 86:1–86:22.
[9] Chain Home, Wikimedia Foundation Inc. [Online] Available: https://en.wikipedia.org/wiki/Chain_Home
[10] Doppler effect, Wikimedia Foundation Inc. [Online] Available: https://en.wikipedia.org/wiki/Doppler_effect
[11] F.-K. Wanget al., “A novel vital-sign sensor based on a self-injection-locked oscillator,” IEEE Trans. Microw. Theory Techn., vol. 58, no. 12, pp. 4112–4120, Dec.2010.
[12] P.-H. Wu, J.-K. Jau, C.-J. Li, T.-S. Horng and P. Hsu, “Phase-and self-injection-locked radar for detecting vital signs with efficient elimination of DC offsets and null points,” IEEE Trans. Microw. Theory Techn., vol. 61, no. 1, pp. 685–695, Jan. 2013.
[13] P.-H. Wu, J.-K. Jau, C.-J. Li, T.-S. Horng, and P. Hsu, “Vital-sign detection Doppler radar based on phase locked self-injection oscillator,” in Proc. IEEE MTT-S Int. Microw. Symp. Dig., Jun. 2012.
[14] P.-H. Juan, C.-Y. Chueh, F.-K. Wang, “Vital signs detection with difference beamforming and orthogonal projection filter based on SIMO-FMCW radar,” IEEE Trans. Microw. Theory Techn., vol. 71, no. 1, pp. 83–92, June. 2022.
[15] P.-H. Juan, C.-Y. Chueh and F.-K. Wang, “Distributed MIMO CW radar for locating multiple people and detecting their vital signs,” IEEE Trans. Microw. Theory Techn., vol. 71, no. 3, pp. 1312–1325, Mar. 2023.
[16] W.-C. Su, P.-H. Juan, D.-M. Chian, T.-S.-J. Horng, C.-K. Wen, and F.-K. Wang, “2-D self-injection-locked Doppler radar for locating multiple people and monitoring their vital signs,” IEEE Trans. Microw. Theory Techn., vol. 69, no. 1, pp. 1016–1026, Jan. 2021.
[17] Y. Yuan, C. Lu, A. Y.-K. Chen, C.-H. Tseng, and C.-T.-M. Wu, “Noncontact multi-target vital sign detection using self-injection-locked radar sensor based on metamaterial leaky wave antenna,” in IEEE MTT-S Int. Microw. Symp. Dig., Jun. 2019, pp. 148–151.
[18] D.-M. Chian, C.-K. Wen, F.-K. Wang, and K.-K. Wong, “Signal separation and tracking algorithm for multi-person vital signs by using Doppler radar,” IEEE Trans. Biomed. Circuits Syst., vol. 14, no. 6, pp. 1346–1361, Dec. 2020.
[19] Y. Yuan, C. Lu, A. Y. Chen, C. Tseng, and C. M. Wu, “Multi-target concurrent vital sign and location detection using metamaterial-integrated self-injection-locked quadrature radar sensor,” IEEE Trans. Microw. Theory Techn., vol. 67, no. 12, pp. 5429–5437, Dec. 2019.
[20] J. Li and P. Stoica, “Efficient mixed-spectrum estimation with applications to target feature extraction,” IEEE Trans. Signal Process., vol. 44, no. 2, pp. 281–295, Feb. 1996.
[21] J. A. Högbom, “Aperture synthesis with a nonregular distribution of interferometer baselines,” Astron. Astrophys. Supplements, vol. 15, pp. 417–426, 1974.
[22] Wang Y, Li J, Stoica P, Sheplak M, Nishida T, “Wideband RELAX and wideband CLEAN for aeroacoustic imaging,” J. Acoust. Soc. America., vol. 115, pp. 757–767, Mar. 2004.
[23] R. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Trans. Antennas Propag., vol. 34, no. 3, pp. 276–280, Mar. 1986.
[24] F.-K. Wang, P.-H. Juan, D.-M. Chian and C.-K. Wen, “Multiple range and vital sign detection based on single-conversion self-injection-locked hybrid mode radar with a novel frequency estimation algorithm,” IEEE Trans. Microw. Theory Techn., vol. 68, no. 5, pp. 1908–1920, May 2020.
[25] F.-K. Wang, J.-X. Zhong and J.-Y. Shih, “IQ signal demodulation for noncontact vital sign monitoring using a CW Doppler radar: A Review,” IEEE J. Electromagn., RF Microw. Med. Biol., vol. 6, no. 4, pp. 449–460, Dec. 2022.
[26] B. Park, O. Boric-Lubecke, and V.M. Lubecke, “Arctangent demodulation with DC offset compensation in quadrature Doppler radar receiver systems,” IEEE Trans. Microw. Theory Techn., vol. 55, no. 5, pp. 1073–1079, May 2007.
[27] C. Li and J. Lin, “Complex signal demodulation and random body movement cancellation techniques for non-contact vital sign detection,” in Proc. IEEE MTT-S Int. Microw. Symp. Dig., 2008, pp. 567–570.
[28] K. Dragomiretskiy and D. Zosso, “Variational mode decomposition,” IEEE Trans. Signal Process., vol. 62, no. 3, pp. 531–534, Feb. 2014.
[29] Y.I. Jang, J.Y. Sim, J.-R. Yang, and N.K. Kwon, “The optimal selection of mother wavelet function and decomposition level for denoising of DCG signal,” Sensors, vol. 21, no. 1851, pp. 1–17, Mar. 2021.
[30] J.-Y. Shih and F.-K. Wang, “Quadrature cosine transform (QCT) with varying window length (VWL) technique for noncontact vital sign monitoring using a continuous-wave (CW) radar,” IEEE Trans. Microw. Theory Techn., vol. 70, no. 3, pp. 1639–1650, Mar. 2022.
[31] S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 7, pp. 674–693, July 1989.
[32] P.-H. Wu, J.-K. Jau, C.-J. Li, T.-S. Horng and P. Hsu, “Phase- and self-injection-locked radar for detecting vital signs with efficient elimination of DC offsets and null points,” IEEE Trans. Microw. Theory Techn., vol. 61, no. 1, pp. 685–695, Jan. 2013.
[33] M. Zakrzewski, H. Raittinen, and J. Vanhala, “Comparison of center estimation algorithms for heart and respiration monitoring with microwave Doppler radar,” IEEE Sensors J., vol. 12, no. 3, pp. 627–634, Mar. 2012.
[34] B.-K. Park, A. Vergara, O. Boric-Lubecke, V. Lubecke, and A. HøstMadsen. (2007, Jan.) Quadrature demodulation with DC cancellation for a Doppler radar motion detector. [Online]. Available: https://goo.gl/q99ybw
[35] W.-C. Su, M.-C. Tang, R. E. Arif, T.-S. Horng, and F.-K. Wang,“Stepped-frequency continuous-wave radar with self-injection-locking technology for monitoring multiple human vital signs,” IEEE Trans. Microw. Theory Techn., vol. 67, no. 12, pp. 5396–5405, Dec. 2019.