無線射頻辨識(Radio Frequency Identification, RFID)技術發展已十分成熟並廣泛的被應用在生活當中。近年來為了提升使用者資訊安全，發展出射頻指紋特徵(Radio Frequency Fingerprint)技術，該技術透過深度演算法學習傳送端硬體製造時的差異對訊號的影響作為訊號特徵，該特徵具有唯一且不易被仿冒的特性，可以進一步提升使用者通訊安全。然而訊號的特徵容易受到傳輸通道的影響而降低演算法的辨識率，因此通道對指紋特徵影響的相關研究近年來也十分受關注。
本論文結合近場通訊高頻無線射頻辨識與射頻指紋特徵辨識技術，透過實際錄製不同感應距離下接收之訊號，分析不同距離下通道對射頻指紋特徵的影響並估測出不同距離下之通道響應，最後對訊號進行通道頻率響應的補償，以降低通道對於射頻指紋特徵的影響，改善通道影響導致辨識率下降的問題。
本論文使用兩種特徵在1cm感應距離下補償後的平均辨識率約為99.65%，1.5cm感應距離下補償後的平均辨識率約為93.2%，2cm感應距離下補償後的平均辨識率約為87.75%。

The development of Radio Frequency Identification (RFID) technology has been widely used in recent years. In order to improve user information security, the Radio Frequency Fingerprint technology has been developed. The technology uses deep learning algorithms to identify the hardware defects on the signal. This feature is unique and irreplaceable. It is not easy to be counterfeited, which can further improve the communication security of users. However, the characteristics of the signal are easily affected by the transmission channel, which reduces the identification rate of the algorithm. Therefore, the related research on the influence of the channel on the fingerprint characteristics has also attracted much attention in recent years.
The paper analyzes the channel's effect on RF fingerprints and estimates the channel response by actually recording the signals received at different sensing distances. Finally, the channel frequency response compensation is performed on the signal to reduce the influence of the channel on the RF fingerprint, and to improve the problem of the decrease of the accuracy caused by the influence of the channel.
The average accuracy of the two features used in this paper after compensation at a 1cm sensing distance is 99.5%, at a 1.5cm sensing distance is 93.2%, and at a 2cm sensing distance is 87.75%.

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