此論文提出了可在超寬頻雷達系統擷取的人體呼吸訊號中，分析且萃取出呼吸訊號特徵之演算法，並實作在訊號處理系統之平台上。在傳統的雷達訊號偵測演算法中，目標大多是以偵測出常用在醫療診斷中的呼吸頻率為主。然而在雷達偵測出的人體呼吸訊號中，除了呼吸頻率之外，含有更多可以提供醫護人員診斷的有用資訊。因此，我們提出了改良過的raised cosine的訊號模型來模擬呼吸的波型，並使用迭代式correlation的方法來萃取呼吸波型的特徵，例如呼吸的強度、吸氣狀態、呼氣狀態以及吸呼氣間停止的狀態。這些特徵的資訊也能視為壓縮後的呼吸訊號，可用在遠端照護監視系統中當作傳輸標的，進而取代一個週期至少上千個取樣資料。而使用此方式更可以減少傳輸的頻寬，以及節省傳送的電力消耗。最後，我們將此演算法以及系統架構實現在一軟硬體可共同運作的訊號處理系統平台上，此平台含有ARM系統之中央處理器以及FPGA邏輯晶片。此裝置可適用於呼吸頻率在0.1赫茲到1赫茲間的呼吸訊號，對於每個呼吸週期分析及萃取其中的特徵。

This paper presents a ultra-wideband (UWB) impulse-radio radar signal processing platform. This platform is integrated with a front-end radar chip for human respiratory feature extraction and signal compression. The conventional radar detection algorithms only extract the respiration rate for medical diagnosis. However, there is more information in the radar-detected respiratory signals which can be useful for medical diagnosis. Thus, this study proposed a modified raised cosine model and an iterative correlation algorithm to extract more respiratory features, such as inspiration and expiration speed, respiration intensity, and respiration holding ratio. Moreover, the extracted features are useful in remote medical monitoring system since they can be seen as compressed respiratory signals. Transmission bandwidth can be saved by transmitting the extracted features instead of lots of sampled data. The proposed algorithm and architecture is designed and implemented on a radar signal processing platform with the ARM processor and FPGA logic array. Human respiratory signals of 0.1 to 1 Hz rate are detected and analyzed along with other information at each period.

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