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Advanced boundary virtual reference algorithm for an indoor system using an active RFID interrogator and transponder

机译:使用有源RFID询问器和应答器的室内系统的高级边界虚拟参考算法

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摘要

Localization of persons and objects has become a necessity in many industrial services. In indoor circumstances, radio frequency identification (RFID) has superior performance. However, radio signals within indoor environments are generally weak, and the tags have very restricted abilities. In addition, the multipath propagation, total cost, and signal interference increase with increasing number of reference tags over a certain limit. Therefore, to improve the performance of indoor positioning for real-time localization, a new active acquisition method for an active RFID system that works at 2.4 GHz has been proposed; it is called the boundary virtual reference (BVIRE) algorithm. It depends on boundary virtual reference tags rather than increasing the number of real reference tags, which in turn reduces the total cost while maintaining the location accuracy. We have implemented a linear regression method to further enhance the positioning accuracy while eliminating the unnecessary tags from the estimation method using event filtering, in which only a few neighbouring reference tags are helpful in deciding the location of the object tag. The experimental results show that our BVIRE algorithm considerably reduces the error estimation compared with previous algorithms. The system has enhanced the positioning accuracy and lowered the total costs. In addition, the localization precision of the proposed approach has been significantly increased to approximately 90.25 % compared with PinPoint algorithm with no additional reference tags or radio frequency interference; this represents a significant improvement over other algorithms.
机译:人员和物体的本地化已成为许多工业服务中的必要条件。在室内环境中,射频识别(RFID)具有出色的性能。然而,室内环境中的无线电信号通常较弱,并且标签具有非常有限的能力。此外,在特定限制内,随着参考标签数量的增加,多径传播,总成本和信号干扰也会增加。因此,为了提高用于实时定位的室内定位的性能,已经提出了一种用于工作在2.4GHz的有源RFID系统的新的有源获取方法。它称为边界虚拟参考(BVIRE)算法。它依赖于边界虚拟参考标签,而不是增加实际参考标签的数量,从而减少了总成本,同时保持了定位精度。我们已经实施了线性回归方法,以进一步提高定位精度,同时从使用事件过滤的估计方法中消除了不必要的标签,在该方法中,只有少数几个相邻参考标签有助于确定对象标签的位置。实验结果表明,与以前的算法相比,我们的BVIRE算法大大减少了误差估计。该系统提高了定位精度并降低了总成本。另外,与没有附加参考标签或射频干扰的PinPoint算法相比,该方法的定位精度已显着提高到大约90.25%。与其他算法相比,这是一个重大改进。

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