在车辆网络中使用RFID系统进行捕获意识密集标签识别
Weijian Xu1, Zhongzhe Song1, Yanglong Sun2
1School of Ocean Information Engineering, Jimei University, Xiamen 361000, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
这项研究引入了一种新的算法,以改善在繁忙的交通中使用射频识别 (RFID) 标签的识别. 先进的捕获感知系统提高了车辆和货物跟踪系统的可靠性和性能.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 无线通信无线通信
背景情况:
- 被动无线电频率识别 (RFID) 系统对于物流和门禁是具有成本效益的.
- 密集的车辆环境会产生复杂的无线通道,导致由于捕获效应导致标签错误.
- 现有的RFID标签识别方法在不利的交通条件下与性能下降作斗争.
研究的目的:
- 为在具有挑战性的交通场景中运行的RFID系统开发一个高效可靠的标签识别算法.
- 解决目前在密集的车辆环境中RFID技术的局限性.
- 确保RFID在不利条件下用于车辆和货物跟踪的成功应用.
主要方法:
- 为RFID系统建立了Nakagami-m分布式通道捕获模型.
- 使用Nakagami-m分布获得捕获概率的表达式.
- 提出了一个先进的捕获感知标签估计方案.
- 进行了广泛的蒙特卡洛模拟来评估算法的性能.
主要成果:
- 拟议的算法表现出强大的适应性色通道.
- 捕获感知方案显著提高了标签识别可靠性.
- 该算法在复杂性和可靠性方面优于现有方法.
- 在密集的环境中成功地减轻了捕获效应引起的标签错误.
结论:
- 开发的捕获感知标签估计方案有效地提高了复杂交通条件下的RFID性能.
- 拟议的模型和算法为可靠的车辆和货物识别提供了强大的解决方案.
- 这项研究有助于在苛刻的物流应用中更广泛地采用RFID技术.
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