調頻連續波雷達FMCW (Frequency Modulated Continuous Wave Radar)已廣泛的被運用在汽車雷達系統中，由於其訊號相互之間的干擾會使得無法有效的偵測出真實目標，因此對於目標物的偵測是很重要的。而在雷達訊號偵測中，由於各種干擾強度是隨機產生的，如果使用傳統的設計採用固定誤警率CFAR(Constant False-Alarm Rate)算法進行偵測，在這種情況下固定臨界值的方式難以保證其偵測效能，會受到多目標物的環境與雜波(clutter)的影響。
因此本論文研究將使用到高階統計(Higher-Order Statistics)的特性，以及使用改善的小波估計(Wavelet Estimation)，把蒐集到的包含真實訊號與雜訊訊號的數據透過連續小波轉換進行降雜訊來調整小波臨界值，找到合適的訊號屬性參數，將雜訊從訊號中分離出來，過過模擬去解決多點雜訊能量問題，達到能夠有效的分辨出偵測目標與降雜訊的效果。

The Frequency Modulated Continuous Wave (FMCW) Radar has been widely used in automotive radar systems, since the interference between their signals will make it impossible to effectively detection the real target, it is very important to detect the target. In radar signal detection, since various interference intensity are randomly generated, if the traditional design uses the CFAR algorithm for detection, in this case, the method of constant threshold value is difficult to ensure its detection performance, and it will be affected by the environment and clutter of multi-target objects.
In this theis, we use the characteristics of HOS and the use of improved wavelet estimation to reduce the noise of the collected data including real signal and noise signal through continuous wavelet transform to adjust the wavelet threshold, find the appropriate signal attribute parameters, separate the noise from the signal, and solve the problem of multi-point noise energy through simulation, so as to effectively distinguish the detection target and denoise .

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