Al-Hameed, AA, Younus, SH, Hussein, AT et al. (2 more authors) (2019) Artificial Neural Network for LiDAL Systems. IEEE Access, 7. pp. 109427-109438. ISSN 2169-3536
Abstract
In this paper, we introduce an intelligent light detection and localization (LiDAL) system that uses artificial neural networks (ANN). The LiDAL systems of interest are MIMO LiDAL and MISO IMG LiDAL systems. A trained ANN with the LiDAL system of interest is used to distinguish a human (target) from the background obstacles (furniture) in a realistic indoor environment. In the LiDAL systems, the received reflected signals in the time domain have different patterns corresponding to the number of targets and their locations in an indoor environment. The indoor environment with background obstacles (furniture) appears as a set of patterns in the time domain when the transmitted optical signals are reflected from objects in LiDAL systems. Hence, a trained neural network that has the ability to classify and recognize the received signal patterns can distinguish the targets from the background obstacles in a realistic environment, especially given the mobility of targets (humans) which distinguishes them from static obstacles (furniture). The LiDAL systems with ANN are evaluated in a realistic indoor environment through computer simulation.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This article is protected by copyright. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/. |
Keywords: | Artificial neural networks , MISO communication , Optical transmitters , Sensors , Indoor environment , MIMO communication , Optical reflection; Neural Network , ANN , Optical indoor localization , VLC systems , people detection , counting , localization |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Funding Information: | Funder Grant number EPSRC EP/H040536/1 EPSRC EP/K016873/1 EPSRC EP/S016570/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Aug 2019 15:46 |
Last Modified: | 25 Jun 2023 21:56 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ACCESS.2019.2933470 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:149468 |