Indoor Real-Time 3-D Visible Light Positioning System Using Fingerprinting and Extreme Learning Machine

Abstract

Photodiode-based (PD-based) visible light positioning (VLP) has become a research focus of indoor positioning technology, while the existing VLP models rarely consider the anti-interference and positioning time of that. In this paper, indoor real-time three-dimensional visible light positioning system using fingerprinting and extreme learning machine (ELM) is proposed to make the system achieve not only high positioning accuracy and elevated anti-interference but also well-behaved real-time ability. In contrast to the positioning system based on K-Nearest Neighbor or Support Vector Machine, the proposed system achieves the highest positioning accuracy and the state-of-the-art positioning speed. Furthermore, the visible light positioning kernel is proposed as a method to reduce the size of the fingerprint database and thus reduce the training time exponentially. Both the simulation and the experiment results show that the proposed system achieves real-time 3-D positioning with high anti-interference. Therefore, this scheme can be considered as one of the effective methods for indoor 3-D positioning. INDEX TERMS Extreme learning machine (ELM), photodiode (PD), positioning fingerprint, real-time positioning, visible light positioning (VLP).

Invoking Deep Learning for Joint Estimation of Indoor LiFi User Position and Orientation

“…Motivated by its successful applications to many practical tasks, both industry and the research communities have advocated the applications of ML in wireless communication, with emphasis on resource management, networking, mobility management and localization [34], [35]. Recently, some works have investigated the use of ML techniques in indoor positioning using LiFi technology, such as K-Nearest Neighbor (KNN) [36], support vector machine (SVM) and extreme learning machine (ELM) [37], [38].…”

“…Once the data set is ready, the goal now is how to obtain "good" mappings between the feature vector that contains the received SNR and the label vector that contains the 3D position and the orientation angles of the UE using the obtained dataset. For such a goal, several learning methods can be applied such as KNN [36], SVM and ELM [37], [38]. In this paper, we will employ, and for the first time, deep ANNs, as it will be presented in the following subsection.…”

Sub-Decimeter VLC 3D Indoor Localization With Handover Probability Analysis

“…Authors in [19] applied two machine learning techniques, namely secondorder linear regression and Kernel Ridge Regression Machine Learning (KRRML) with sigmoid function data preprocessing for VLC-based indoor localization. Additionally, [20], [21] employ Weighted K-Nearest Neighbor (WKNN) to estimate the location of the receiver in a VLC indoor environment, while [22] uses extreme machine learning for visible light positioning.…”

Impact of a Photodiode’s Angular Characteristics on RSS-Based VLP Accuracy

“…Machine learning methods fit the RSS − d relation on a (sparse) collection of training fingerprints. References [12], [13] both ensure accurate localisation using feed-forward neural networks (FNNs). The authors of [12] obtain a factor 7.6 accuracy enhancement with respect to traditional trilateration.…”