Performance Bounds on Passive Indoor Positioning Using Visible Light

Majeed, Khaqan; Hranilovic, Steve

doi:10.1109/jlt.2020.2966365

Cited by 19 publications

(9 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…VLC localization has also attracted substantial attention due to the broad use of light-emitting diodes (LED) for indoor lighting. Optical channel impulse response (OCIR)-based VLC positioning methods benefit from the largely static and deterministic optical wireless channel for localization [1][2][3][4][5]. This paper uses an artificial neural network (ANN) to evaluate the effectiveness of employing multipath reflection information within the OCIR with varying levels of temporal resolution for localization.…”

Section: Introductionmentioning

confidence: 99%

Optical Channel Impulse Response-Based Localization Using An Artificial Neural Network

Hosseinianfar¹,

Rabbani²,

Bradnt-Pearce³

2022

Preprint

View full text Add to dashboard Cite

Visible light positioning has the potential to yield sub-centimeter accuracy in indoor environments, yet conventional received signal strength (RSS)-based localization algorithms cannot achieve this because their performance degrades from optical multipath reflection. However, this part of the optical received signal is deterministic due to the often static and predictable nature of the optical wireless channel. In this paper, the performance of optical channel impulse response (OCIR)-based localization is studied using an artificial neural network (ANN) to map embedded features of the OCIR to the user equipment's location. Numerical results show that OCIR-based localization outperforms conventional RSS techniques by two orders of magnitude using only two photodetectors as anchor points. The ANN technique can take advantage of multipath features in a wide range of scenarios, from using only the DC value to relying on high-resolution time sampling that can result in sub-centimeter accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Optical Channel Impulse Response-Based Localization Using An Artificial Neural Network

Hosseinianfar¹,

Rabbani²,

Bradnt-Pearce³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Recently, visible light positioning (VLP) has been getting attention as it features high positioning accuracy in an already existing infrastructure [3][4]. There are many VLP algorithms proposed in the literature that have led to a rapid advancement in this area [5]- [9]. In this work, we utilized VLP to assist with the handover decision that would result in an increased performance while maintaining a lower number of handovers, as will be shown in the following sections.…”

Section: Introductionmentioning

confidence: 99%

Location Aware Vertical Handover in a VLC/WLAN Hybrid Network

Zeshan¹,

Baykaş

2021

IEEE Access

View full text Add to dashboard Cite

Visible light communication (VLC) has emerged as a promising technology for wireless communication as it offers higher data rates and secure data transmission along with providing indoor illumination. However, VLC is restricted by the line of sight (LoS) nature of the optical channel that consequently results in light path blockages. Therefore, an effective solution would be to combine VLC with a radio frequency (RF) system to form a hybrid VLC/RF network that would take into account the preferences of an end-user with the practicality of implementation. In such networks, an efficient vertical handover (VHO) technique is the most critical element as it ensures a seamless transition between the two networks. In this work, we propose a vertical handover technique that utilizes the user's location information to make a handover decision. We found that the frequency of light path blockages increases with the increasing number of users in a confined space, resulting in significant performance deterioration. This additional information is then utilized so that the VHO algorithm effectively selects the most feasible network. The proposed algorithm has been tested against the immediate vertical handover algorithm (I-VHO) and the dwell vertical handover algorithm (D-VHO) with two different dwell times. The average number of handovers, quality of experience (QoE), and packet loss have been set as performance metrics. We show from several simulation scenarios that the proposed method results in a fewer number of handovers while maintaining higher QoE and lower packet loss.

show abstract

“…For instance, Majeed et al [14] propose an indoor location method using LED luminaries, where a passive user participation is not needed; however, specific LED lights are. Guan et al [15] use visible light communication and specific niche of Robot Operating System (ROS) to develop an ILS.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Indoor Location Through Magnetic Field Data: An Approach Based On Convolutional Neural Networks

Galván-Tejada

Zanella-Calzada

García-Domínguez

et al. 2020

IJGI

View full text Add to dashboard Cite

Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (i.e., wifi, bluetooth, rfid, sound, movement,etc).In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user's location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set.

show abstract

Performance Bounds on Passive Indoor Positioning Using Visible Light

Cited by 19 publications

References 40 publications

Optical Channel Impulse Response-Based Localization Using An Artificial Neural Network

Optical Channel Impulse Response-Based Localization Using An Artificial Neural Network

Location Aware Vertical Handover in a VLC/WLAN Hybrid Network

Estimation of Indoor Location Through Magnetic Field Data: An Approach Based On Convolutional Neural Networks

Contact Info

Product

Resources

About