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- W3212583327 abstract "In this work, a novel positioning algorithm based on a long short term memory-fully connected network (LSTM-FCN) is proposed to improve the performance of an indoor visible light positioning (VLP) system. Using the proposed LSTM-FCN based positioning algorithm, the VLP system with a single light emitting diode (LED) and multiple photodetectors (PDs) was implemented. On this basis, the positioning performance of the established VLP system using proposed LSTM-FCN, traditional FCN and support vector regression (SVR) based algorithm is investigated and compared. It is demonstrated that the VLP system using the proposed LSTM-FCN based algorithm has better performance than that using other machine learning algorithms. As a result, an average positioning error of 0.92 cm and a maximum positioning error of less than 5 cm can be obtained for the established VLP system." @default.
- W3212583327 created "2021-11-22" @default.
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- W3212583327 date "2021-11-24" @default.
- W3212583327 modified "2023-10-18" @default.
- W3212583327 title "High accuracy indoor visible light positioning using a long short term memory-fully connected network based algorithm" @default.
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- W3212583327 doi "https://doi.org/10.1364/oe.445389" @default.
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