Using Data Pre-Processing and Convolutional Neural Network (CNN) to Mitigate Light Deficient Regions in Visible Light Positioning (VLP) Systems | PERPUSTAKAAN UNIVERSITAS KATOLIK PARAHYANGAN

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Using Data Pre-Processing and Convolutional Neural Network (CNN) to Mitigate Light Deficient Regions in Visible Light Positioning (VLP) Systems

Hsu, Li-Sheng - Nama Orang; Liu, Yang - Nama Orang; Yeh, Chien-Hung - Nama Orang; Chow, Chi-Wai - Nama Orang; Chang, Yun-Han - Nama Orang; Tsai, Deng-Cheng - Nama Orang; Lin, Yuan-Zeng - Nama Orang; Wang, Yi-Chang - Nama Orang; Chen, Yi-Yuan - Nama Orang;

New systems and technologies, such as Internet-of-Things (IOT) may require high reliability and high accuracy indoor positioning and tracking of persons and devices in indoor areas. Among different visible-light-positioning (VLP) schemes, received-signal-strength (RSS) scheme is relatively easy to implement. RSS VLP scheme can provide high accuracy positioning if the optical channels between the Txs and Rxs, as well as the received optical powers of different LEDs are accurately known. Unfortunately, these conditions are not easy to achieve in practice. Due to the limited field-of-view (FOV) of the LED lamps, light deficient regions will happen. This light deficient region could be large and significantly affect the positioning accuracy when performing 3-dimentional (3-D) VLP since at these light deficient regions, very weak or even no optical signal is received. In this work, we put forward and demonstrate the RSS VLP system utilizing data pre-processing and convolutional neural network (CNN) to mitigate light deficient regions in VLP system. Traditional ANN model and linear regression (LR) model are also compared with the CNN model, and the results illustrate that the proposed scheme outperforms the other schemes by not only improving the positioning accuracy, but also the error distribution uniformity.

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Barcode		Tipe Koleksi	Nomor Panggil	Lokasi	Status
art145423	null	Artikel		Gdg9-Lt3	Tersedia namun tidak untuk dipinjamkan - No Loan

Informasi Detail

Judul Seri: JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.40 No.17 September 1 2022
No. Panggil: -
Penerbit: : .,
Deskripsi Fisik: p. 5894-5900
Bahasa: English
ISBN/ISSN: -
Klasifikasi: NONE
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: VISIBLE LIGHT COMMUNICATION (VLC)
OPTICAL WIRELESS COMMUNICATION (OWC)
CONVOLUTIONAL NEURAL NETWORK (CNN)
VISIBLE LIGHT POSITIONING (VLP)
Info Detail Spesifik: DOI: 10.1109/JLT.2022.3184931
Pernyataan Tanggungjawab: Li-Sheng Hsu, Deng-Cheng Tsai, Chi-Wai Chow, Yang Liu, Yun-Han Chang, Yuan-Zeng Lin, Chien-Hung Yeh, Yi-Chang Wang, Yi-Yuan Chen

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