A Single-Anchor Visible Light Positioning System Based on Fingerprinting and Deep Learning

Jiale Jiang, K. Zhao, Jiasheng Zhou, X. Cao, Zhuang Yuan
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Abstract

Due to severe signal obstruction, the global navigation satellite system is unable to work indoors. Visible light positioning, as an alternative technology for indoor positioning, has gained widespread attention in recent years due to its low cost and environmental friendliness. Among these, the visible light single anchor positioning method based on light-emitting diode arrays has shown great potential as it can simultaneously provide lighting and positioning. The rise of artificial intelligence has provided new methods for indoor positioning.This article focuses on the single anchor visible light fingerprinting-based positioning technology and uses a multi-layer perceptron-based method to maximize its performance. In addition, in terms of hardware design, we focus on improving the receiver's integration, making it applicable to a wider range of scenarios through size reduction and cost control. Finally, the designed hardware and the proposed method are evaluated in the space range of 320 cm* 560 cm* 270 cm. When compared with the traditional nearest neighbor, k-nearest neighbor, and weighted k-nearest neighbor methods, the experimental results show that the proposed method exhibits significant advantages in performance. The average positioning accuracy in the real scene can reach 34cm.
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基于指纹识别和深度学习的单锚可见光定位系统
由于严重的信号阻塞,全球卫星导航系统无法在室内工作。可见光定位作为室内定位的一种替代技术,由于其成本低、环境友好等优点,近年来受到了广泛的关注。其中,基于发光二极管阵列的可见光单锚定位方法由于能够同时提供照明和定位而显示出巨大的潜力。人工智能的兴起为室内定位提供了新的方法。本文主要研究基于单锚点可见光指纹的定位技术,并采用基于多层感知器的方法使其性能最大化。此外,在硬件设计方面,我们注重提高接收机的集成度,通过减小尺寸和控制成本,使其适用于更广泛的场景。最后,在320 cm* 560 cm* 270 cm的空间范围内对所设计的硬件和所提出的方法进行了评估。实验结果表明,与传统的最近邻、k近邻和加权k近邻方法相比,该方法具有显著的性能优势。真实场景平均定位精度可达34cm。
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