FMCW 雷達相較於CW 雷達多出測距能力，透過一些訊號處理的方法更能使FMCW 雷達擁有有多個目標物體的分析能力，因此可以用於分析多個目標的生理訊號、運動狀態等領域；但FMCW 雷達在訊號處理上需要非常大量的運算資源，例如FFT 轉換等，造成在運算上會有所延遲，無法及時反應結果；本文嘗試了基於兩種不同機制的生理訊號評估，分別針對這兩種方法的特性及需求各別設計FMCW 雷達訊號處理平行演算法，透過GPU 平行加速的方式將執行時間最小化。

Compared to CW Radar, FMCW radar has range measurement capability. By employing some signal processing method, FMCW radar can do multiple target tracking so that it can apply to the vital sign and motion state tracking of multiple targets. But FMCW radar
needs massive processing resources; for example, it needs plenty of FFT processing. It
leads to the delay in processing, making it difficult to achieve real-time processing. In this thesis, we employ two different evaluation methods for evaluating vital signs; consequently, we design two parallel algorithms of FMCW radar signal processing for both methods and minimize the execution time through GPU parallel acceleration.

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