無線射頻標籤辨識(RFID)系統因其具有快速、便利及無需接觸物體等優勢，現已廣泛應用在自動辨識系統中。在現實環境中存在捕捉效應與雜訊干擾現象，捕捉效應為多個標籤同時回傳訊號時因某一標籤訊號較強而導致讀取器可成功辨識此標籤，如此產生成功時槽，且讓其它標籤被隱藏；雜訊干擾為辨識環境處於一易受干擾影響的狀態，造成標籤在回傳訊號時無法被正常辨識，讀取器便會將原本成功時槽誤認為碰撞時槽。過去的多數研究都假設此二種現象不存在而予以忽視，然此確實會對RFID辨識效能造成影響。由於捕捉效應與雜訊干擾的發生機率跟最佳訊框長度的設定有關，因此如何有效且準確估計其發生機率變得相當重要，本論文同時考量此二種現象，提出雙訊框(Double Frame, DF)演算法，可以準確估計環境中捕捉效應機率與雜訊干擾機率。DF方法之優點為運算複雜度低且估計準確，其捕捉效應機率的平均估計誤差為0.84%，比現有方法CAE(Capture Aware Estimation)好上19.58%；其雜訊干擾機率的平均估計誤差為0.75%，比現有方法ERE-ABS(Error Resilient Estimation and Adaptive Binary Selection)好上20.66%。

The Radio Frequency Identification (RFID) technique is widely applied into the
automated identification system because it has immediate, convenient and contactless identification. Capture Effect and Noise Interference exist in a realistic environment. Capture Effect is that the reader successfully identifies one tag’s ID from many tags’ responses because its signal is much stronger than others. Thus the other tags will not be further identified, i.e., hidden. Noise Interference is the wireless transmission is in an error-prone environment, causing that the tag’s signal encounters errors. Therefore the reader can’t identify the tag ID, and regards a successful slot as a collision slot. Most papers assumed that these two phenomena do not appear and can be ignored, but they really exist in a realistic environment and significantly affect the performance of RFID identification. Since the opportunities of Capture Effect and Noise Interference will influence on the setting of the optimal frame length, estimating them effectively and accurately becomes important. This thesis considers these two phenomena and proposes the Double Frame (DF) algorithm, which can accurately estimate the probabilities of Capture Effect and Noise Interference. DF has not only low computational complexity but also high accuracy. The average error ratio of Capture Effect estimation is 0.84%, which is 19.58% better than the existing method CAE (Capture Aware Estimation). The average error ratio of Noise Interference estimation is 0.75%, which is 20.66% better than the existing method ERE-ABS (Error Resilient Estimation and Adaptive Binary Selection).

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