本篇論文以頻率調變連續波雷達架構提出一Ensemble empirical mode decomposition –Cancelation – Ensemble empirical mode decomposition – Principal component analysis – Cross-correlation (ECEPC)演算法用於消除量測生理訊號時非穩態雜波造成之影響，如待測者自身的隨機晃動或是待測者旁有他人走過等情況產生的非穩態雜波。由此演算法，能將量測到生理訊號時所接收到的非穩態雜波訊號去除，並萃取出生理訊號。未來預期可以應用在嬰兒生命徵象及醫院等環境；由於去除非穩態雜波的訊號代表能夠降低錯誤預警的機率，因此未來的應用面相會更貼近於實際的應用場景。
在本篇論文中，包含了完整的頻率調變連續波雷達系統的理論分析，其中包含以下三種情況：（一）理想情況下 （二）空間中存在靜態雜波下 （三）空間中存在非穩態雜波下，並分析不同情況下生理訊號如何被影響。演算法的部份會探討提出之演算法組合的思維，最終得出為何要使用此演算法流程。同時利用模擬及實驗來驗證提出的演算法，所驗證的狀況有兩種：1. 待測者本身隨機晃動，2. 待測者量測時旁邊有他人走過，所提出的ECEPC相較於傳統解調方法，如複數訊號解調，在非穩態雜波的影響下，其解調方法會受到諧波的劇烈影響，使得生理訊號的訊雜比大幅下降，在本篇的人體本身隨機晃動實驗中，複數訊號解調甚至連呼吸訊號都無法辨識，而所提出之演算法能有效的去除非穩態雜波對生理訊號造成的影響。 在本篇實驗利用所提出之演算法相對於複數訊號解調在呼吸的以及心跳的訊號干擾比平均提升了15.81 dB和26.18 dB，並且能夠在頻譜上明顯的看出心跳以及呼吸成分，而在非穩態雜波的量測環境下，從參考文獻中得到的心跳頻率錯誤率為12.39%，但是提出之演算法最大也不超過6.11%，由此可見所提出之演算法的有效性。

This thesis proposed a ECEPC algorithm to eliminate the influence of non-stationary clutter when measuring vital sign based on FMCW radar. By using proposed algorithm, the non-stationary clutter can be canceled. Since non-stationary clutter is canceled, the probability of false alarm would decrease, and the vital sign radar could be used in practical scenarios.
In this thesis, the FMCW radar theory of three different environments are described and analyzed. The algorithm chapter discuss the designing of ECEPC, and the theory of adopted algorithms also be described, including are EEMD, PCA and cross-correlation. Simulation and experiments are carried out to verify ECEPC. The simulation including two cases, one is the subject randomly moving his body, the other is the other person passing by the subject. Under the influence of non-stationary clutter, CSD would suffer from harmonic which severely decrease the SNR. In the experiment of subject randomly moving his body, CSD even can’t identify respiration on the spectrum; however, ECEPC can recognize not only the respiration, but also the heartbeat on the spectrum.
In order to quantize the performance of ECEPC, SIR is adopted to evaluate the performance of ECEPC and CSD. Compared with CSD, ECEPC increasing 15.81 dB of respiration SIR and 26.18 dB of heartbeat SIR in average. Refer to the reference, the heartbeat error rate is 12.39% in non-stationary clutter scenario; however, the heartbeat error rate of ECEPC is not greater than 6.11%. The experiment results prove that ECEPC is an effective way to solve non-stationary clutter issue.

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