VLC localization: deep learning models by Kalman filter algorithm combined with RSS

Abstract

AbstractIn this paper, a new framework is presented for indoor visible light communication (VLC) system, based on Yolo v3, EfficientNetB3, and DenseNet121 deep learning (DL) models, as well as an optimization strategy. The proposed framework consists of two steps: data collecting and DL model training. To start, data is acquired using MATLAB and Kalman Filtering (KF) with averaging approaches. Second, the received signal strength (RSS) is employed as the DL models input, with the Cartesian coordinates as the DL models output. The averaging RSS approach combined with KF algorithm are used in the suggested framework. This work introduces the impacts of Non-Line-of-Sight (NLoS) for initial reflection and Line-of-Sight (LoS) based on the three mentioned DL models. Furthermore, we used Bayesian optimization and automatic hyper-parameter (HP) optimization to increase system efficiency and to reduce positioning error in DL models. The obtained results show that the models outperform existing the HP-RSS-KF-LoS-DL models in terms of localization error when compared to traditional RSS signal-based localization techniques. Many performance indicators are considered to evaluate the proposed framework resiliency, including accuracy (ACC), area under the curve (AUC), sensitivity (Se), and precision (Pr), as well as F1-score, root mean square error (RMSE), training, and testing time. The DL models are generated and trained using Python software on a Kaggle Notebook GPU cloud (2 CPU cores and 13 GB RAM). The achieved results are: 99.99% ACC, 99.98% AUC, 98.88% Se, 98.98% Pr, 99.97% F1-score, 0.112 cm RMSE, and 0.29 s testing time. The proposed system could be easily deployed for autonomous applications, based on the analysis of the experimental data. Several applications can be used depending on enhancing the localization of VLC system in military systems, underwater systems, and indoor systems like hospitals, hotels, libraries and malls.