Real-time and Nearly Ideal Hologram for RFID-based Indoor Localization

Haonan Chen, Dong Wang
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Abstract

Through the investigation of the mathematical model of hologram-based indoor localization system using RFID, this paper reveals two potential deficiencies about accuracy and gives the machine learning interpretation of the model. Exploiting the methods from machine learning and the thought of hierarchy, the output accuracy and the efficiency of the model can be further boosted. Simulation and experiment show that the enhanced model can halve mean error and attain 9x execution speed improvement.
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基于rfid的室内定位实时、近理想全息图
通过对基于全息图的RFID室内定位系统数学模型的研究,揭示了该系统在精度上的两个潜在缺陷,并给出了该模型的机器学习解释。利用机器学习的方法和层次思想,可以进一步提高模型的输出精度和效率。仿真和实验表明,改进后的模型可以使平均误差减半,执行速度提高9倍。
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