The use of radio-frequency identification (RFID) technology in supply chain has been a fairly mature application in recent years, which can be extended to the field of carrier management for the inventory and access control of sensitive files and mobile storage medium. To address the inherent defects of false readings of RFID, we present RF-AMOC, a tag movement identification system that leverages the signal variation patterns between the opposite antennas and the tag to accurately determine whether someone takes the sensitive carrier out of the room or just the normal carrier usage activity in the room. Particularly, we focus on two kinds of signal variation modes: Direct side models, where the RSSI is sensed by one antenna on the tag side, and obstruction side models, where the RSSI is sensed by the other antenna that was obstructed by the person. Then, Pearson Coefficient and crest comparison algorithms are adopted to match the theoretical and actual RF-signal curves on the two sides, respectively. Additionally, a starting point acquisition method is proposed to extract the meaningful time period. A prototype of RF-AMOC is realized in two different environments with various persons, and the results validate that it is superior in terms of sensitivity and specificity with strong robustness.

中文翻译：

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射频-AMOC

射频识别（RFID）技术在供应链中的应用是近年来比较成熟的应用，可以扩展到敏感文件和移动存储介质的库存和访问控制的运营商管理领域。为了解决 RFID 错误读取的固有缺陷，我们提出了 RF-AMOC，这是一种标签移动识别系统，它利用相对天线和标签之间的信号变化模式来准确确定是否有人将敏感载体带出房间或只是房间内的正常运营商使用活动。特别是，我们关注两种信号变化模式：直接侧型号，其中 RSSI 由标签侧的一根天线感应，以及障碍侧模型，其中 RSSI 由被人阻挡的另一根天线感应。然后，分别采用皮尔逊系数和波峰比较算法来匹配两侧的理论和实际射频信号曲线。此外，提出了一种起点获取方法来提取有意义的时间段。RF-AMOC的原型在不同人员的两种不同环境中实现，结果验证了它在灵敏度和特异性方面具有优越性，具有很强的鲁棒性。