可见光无线定位系统中的机器学习研究进展

Pan Zhu, Shibo Gong, Xuerui Zhao, Xinhuan Sun
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引用次数: 0

摘要

在人工智能扎实发展的时代,机器学习方法与无线定位系统的结合,使研究人员提出了新的研究思路,以提高无线定位在空间和平面上的性能。本文描述了无线定位系统和相关的传统定位方法,并对最近相关定位问题中的机器学习方法进行了分类和分析。无线定位系统的性能根据机器学习的三种传统分类进行评估:监督、无监督和深度学习。我们还提供了多年来在机器学习和无线定位系统相关文章中使用的算法的见解。最后,对基于机器学习方法的无线定位系统进行了综述,并对未来无线光学定位系统的发展方向进行了展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Review: Machine Learning in Visible Light Wireless Positioning System
In the era of solid development of artificial intelligence, the combination of machine learning methods and wireless localization systems has led researchers to new research ideas to improve the performance of wireless localization in space and the plane. In this paper, we describe wireless localization systems and related traditional localization methods, in addition to classifying and analyzing a summary of machine learning methods in recent related localization problems. The performance of wireless localization systems is evaluated according to three traditional classifications of machine learning: supervised, unsupervised, and deep learning. We also provide insight into the algorithms used in machine learning and wireless localization system-related articles over the years. Finally, we provide an overview summary of wireless localization systems based on machine learning methods and give directions for the future development of wireless optical localization systems.
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