Indoor Visible Light Positioning System Research Articles

Environmental Impacts on the RSS-Based Visible Light Indoor Positioning System

To address the shortcomings of the global positioning system (GPS) operating within indoor scenarios, a range of indoor positioning systems (IPS) have been proposed. Among these, visible light IPS garners substantial research interest for its potential as a cost-effective IPS solution. However, susceptibility to environmental influences like external light and non-line-of-sight that can significantly affect its performance hindered its adoption. Hence, a simulation framework capable of simulating these influences becomes crucial in supporting the effective deployment and promoting the adoption of visible light IPS. This paper investigates the feasibility of using a simulation framework to replicate environmental factors affecting the system, including external optical sources and blocked transmitters. The research involved the development of a received signal strength (RSS) based visible light IPS prototype and an associated simulation framework. Subsequently, the prototype was tested in three distinct environments: no influences, with an external optical source, and non-line-of-sight. The prototype achieved an accuracy of 0.0836 m, 0.3541 m, and 1.1519 m for 50% of the time respectively in these three environments. Ultimately, the simulation framework developed is capable of reproducing similar effects for all three environments with a difference in the accuracy of 76.08%, 74.86% and 867%, respectively. Thus, the findings support the use of the developed simulation framework for the effective development of visible light IPS in environments with no influences and with external optical sources.

Three-dimensional visible light positioning through fisheye camera

Abstract Visible light can be utilized to achieve indoor high-precision positioning. However, most visible light positioning (VLP) systems only achieve two-dimensional (2D) positioning and require a dense LED layout as the transmitter. Therefore, we propose a novel three-dimensional (3D) visible light indoor positioning system, which uses the fisheye camera as the receiver, designs an LED width comparison method to reduce the requirement of LED density, and realizes 3D positioning. When images captured by the fisheye camera distort, we use the checkerboard calibration method to correct the distortion parameters of the fisheye camera. The parameters can be used to improve the impact of image distortion on the positioning effect. For the LED width comparison method, we set up a discrete height-width database, fit a curve between height and LED width, and calculate the receiver height according to the linear relationship. Extensive results show that the positioning system can achieve a 3D positioning with 9.66cm average error.

Machine Learning Based Visible Light Indoor Positioning With Single-LED and Single Rotatable Photo Detector

In recent years, visible light positioning (VLP) systems have attracted considerable attention because they do not require additional infrastructures. However, most existing researches on the VLP ignore the impact of wall diffuse reflection, which can lead to the dramatical decrease of the position accuracy performance near the indoor walls and corners. In this paper, we design an indoor VLP system with one single light-emitting diode (LED) and a rotatable photodetector (PD), and then propose an indoor VLP algorithm based on machine learning (ML) methods with concern for the indoor reflection of the optical propagation. The proposed positioning process is implemented via two stages: area classification and positioning. During the area classification stage, by using the random forest (RF) algorithm, the entire room is divided into one interior area and four wall or corner zones. In the interior area, the rotatable PD is directly used to determine the target location. In the four wall or corner zones, a hybrid positioning algorithm based on the extreme learning machine (ELM) and the density-based spatial clustering of applications with noise (DBSCAN) is developed to improve localization accuracy near the indoor walls and corners. Simulation results show that by using the proposed indoor VLP system with the rotatable PD and the hybrid algorithm, the maximum and averaged positioning errors of wall or corner zones drop from 137.96 cm and 15.63 cm, to 38.34 cm and 1.43 cm, respectively, and the averaged positioning error of the whole room decreases from 11.97 cm to 1.74 cm.

Indoor High-Precision 3D Positioning System Based on Visible-Light Communication Using Improved Whale Optimization Algorithm

Visible-light communication (VLC) is a promising method for indoor positioning. The received signal strength algorithm is the most widely used localization algorithm in visible-light positioning (VLP) systems. However, in a VLP system, the photodiode (PD) will have a small rotation angle during movement, which will result in a massive positioning error ignoring the angle. In this study, a three-dimensional (3D) indoor VLP system using an improved whale optimization algorithm (IWOA) is proposed to reduce the error caused by the PD rotation. Firstly, the model of the VLC system with the PD rotation angles is introduced. Secondly, a novel IWOA with an elite opposition-based learning strategy and Lévy flight strategy is proposed. The convergence speed and accuracy of the WOA are improved. Lastly, the IWOA algorithm is efficiently utilized to address the problem with the PD rotation in the indoor VLP system. Simulation results show that the average error of 3D positioning is 2.14 cm with no PD rotation. When the PD has a rotation angle, the average positioning error estimated by ignoring the rotation angle is 27.14 cm, while that estimated by considering the rotation angle is 7.85 cm. In the VLP system, the positioning error with the PD rotation angle is effectively reduced by the proposed algorithm, which can be applied in a variety of indoor location scenes.

Visible Light Positioning Based on Collaborative LEDs and Edge Computing

The proliferation of the Internet of Things pushes the visible light positioning (VLP) system research. However, the existing positioning systems still have the following problems: 1) when the smartphone (receiver) is rotated or tilted during the positioning, existing collaborative LEDs’ positioning algorithms fail and 2) for different smartphone application scenarios, there is not an effective resource management solution between the server and the client. Therefore, in this article, we design and implement a robust and flexible indoor VLP system based on collaborative LEDs and edge computing. First, we propose the enhanced collaborative LEDs’ positioning algorithm, which uses the indoor hidden location information to obtain the rotation angle and tilt angle of the receiver, to achieve the robust system positioning. Then, we use the edge computing solution to balance between bandwidth resources and computing resources and propose a flexible functional segmentation scheme for different smartphone application scenarios. Finally, we conduct experimental tests to evaluate the positioning system performance by landmark decoding rate, positioning accuracy, and segmentation analysis. Test results show that the designed positioning system can achieve centimeter-level positioning. Meanwhile, the smartphone can exchange the least bandwidth resources for the most computing resources under Scheme-3.

Demonstration of Three-Dimensional Indoor Visible Light Positioning with Multiple Photodiodes and Reinforcement Learning.

To provide high-quality location-based services in the era of the Internet of Things, visible light positioning (VLP) is considered a promising technology for indoor positioning. In this paper, we study a multi-photodiodes (multi-PDs) three-dimensional (3D) indoor VLP system enhanced by reinforcement learning (RL), which can realize accurate positioning in the 3D space without any off-line training. The basic 3D positioning model is introduced, where without height information of the receiver, the initial height value is first estimated by exploring its relationship with the received signal strength (RSS), and then, the coordinates of the other two dimensions (i.e., X and Y in the horizontal plane) are calculated via trilateration based on the RSS. Two different RL processes, namely RL1 and RL2, are devised to form two methods that further improve horizontal and vertical positioning accuracy, respectively. A combination of RL1 and RL2 as the third proposed method enhances the overall 3D positioning accuracy. The positioning performance of the four presented 3D positioning methods, including the basic model without RL (i.e., Benchmark) and three RL based methods that run on top of the basic model, is evaluated experimentally. Experimental results verify that obviously higher 3D positioning accuracy is achieved by implementing any proposed RL based methods compared with the benchmark. The best performance is obtained when using the third RL based method that runs RL2 and RL1 sequentially. For the testbed that emulates a typical office environment with a height difference between the receiver and the transmitter ranging from 140 cm to 200 cm, an average 3D positioning error of 2.6 cm is reached by the best RL method, demonstrating at least 20% improvement compared to the basic model without performing RL.

Research on visible light indoor positioning algorithm based on fire safety

From the perspective of ensuring life safety, combined with the advantages of high-speed time response and energy conservation of white light emitting diodes (LEDs), the visible light indoor positioning algorithm based on fire safety is proposed in the paper. First, the model is designed which needs three LED lights arranged in a straight line and positioned in the geographically north direction on the top of the model. Then, the proposed algorithm is discussed and analyzed when the camera is located at the center of the model and facing north, when the camera is located at the center of the model and the angle is rotated, and when the camera is located at any position of the model, respectively. It can accurately calculate the current position of the camera, its response speed is fast and the positioning accuracy is high. Furthermore, this paper also verifies the practicability and reliability of the algorithm by designing the visible light indoor positioning system based on fire safety rescue in natural environment and smoke environment. The experimental results show that the positioning error does not exceed 0.70 cm in smoke environment.

Research on visible light indoor positioning technique using two light sources andspecular reflection cancellation

Here, a high accuracy camera-based visible light indoor positioning system (IPS) has been investigated. A novel two LEDs-based positioning algorithm is proposed aiming at solving the problems of positioning ambiguity with a good trade-off between high accuracy and computational complexity. The simulation results show that the positioning errors of the proposed 2-LED positioning algorithm achieves 2 cm when the resolution of the image sensor (IS) exceeds 2500 pixels/line. A specular reflection cancellation scheme is presented to further reduce the positioning errors when specular reflection interference occurs. The simulation results show that the positioning errors can be reduced to 2.71 cm after using the specular reflection cancellation.

Implementation and improvement of Bayes’s theorem for indoor visible light positioning system

Visible light radiated from light-emitting diodes is used to estimate the position of the receiver in a positioning system indoors. Wall reflection and noise are inevitable, which result in an error in the visible light indoor positioning system. Some indoor positioning algorithms based on fingerprint are compared through simulation. An improved indoor positioning algorithm of Bayesian theory is proposed. After using a Gaussian filter, several grid points that are close to each other are selected to be a cluster, and then the posterior probability of each cluster is calculated by the Bayesian algorithm. The cluster with the largest posterior probability is obtained to estimate the receiver’s position. The improved algorithm simplifies the Bayesian algorithm and improves the positioning accuracy, with an average error of 0.18 m.

Demonstration of a Low-Complexity Indoor Visible Light Positioning System Using an Enhanced TDOA Scheme

In this paper, a low-complexity time-difference-of-arrival (TDOA) based indoor visible light positioning (VLP) system using an enhanced practical localization scheme based on cross correlation is proposed and experimentally demonstrated. The proposed TDOA scheme offers two advantages: 1) the use of virtual local oscillator to replace the real local oscillator for cross correlation at the receiver side so as to reduce the hardware complexity; 2) the application of cubic spline interpolation on the correlation function to reduce the rigorous requirement on the sampling rate and to enhance the time-resolution of cross correlation. In order to achieve the high positioning accuracy with minimum implementation complexity, parameter optimization is first performed in terms of sampling rate, interpolation factor, and data length for correlation. Using the obtained optimal parameters, we demonstrate a low-complexity indoor two-dimensional VLP system using the correlation-based TDOA scheme in a coverage area of 1.2 $\times$ 1.2 m $^{2}$ with a height of 2 m. The experimental results validate the feasibility of the proposed TDOA scheme, and an average positioning accuracy of 9.2 cm is achieved with a sampling rate of 500 MSa/s, an interpolation factor of 100 and a data length of 250 k samples.

The LED-ID Detection and Recognition Method Based on Visible Light Positioning Using Proximity Method

Complementary metal-oxide-semiconductor (CMOS) sensor based visible light positioning (VLP) has been widely studied in recent years due to its high robustness and high precision. In most researches about CMOS sensor based VLP, researchers always focus on the high-precision positioning algorithm but ignore that the accuracy of LED-ID detection and recognition plays a more important role in a VLP system. Without the correct recognition of LED-ID, the positioning algorithm would be meaningless no matter how effective it is. In addition, high-precision positioning is not required in most applications since it is enough for people to know just the approximate location. To solve these problems, in this paper, an LED-ID detection and recognition method based on visible light positioning using proximity method is propose. Different from the traditional LED-ID coding and decoding method, we create different features for different LED-ID, and use a machine learning method to identify the LED-ID once the feature extraction and selection of the LED image is achieved with an image processing method. It is the first time the machine learning method is used for LED-ID recognition in VLP. Moreover, we use a proximity-based positioning method to get the approximate location since it is easy to obtain once the LED-ID is recognized. The studies we have demonstrated shows that the proposed method can achieve high LED-ID recognition rate, and provide enough unique LED-ID for variable large-scale indoor VLP system. Furthermore, with the development of camera technology, the number of the unique LED-ID and the maximum recognizable distance would increase. Therefore, this scheme may be considered as one of the useful LED-ID detection and recognition method for visible light positioning in the future.

A three-dimensional indoor positioning technique based on visible light communication using chaotic particle swarm optimization algorithm

In this paper, an indoor visible light localization system based on improved chaotic particle swarm optimization (CPSO) is proposed to achieve indoor 3-D positioning. In the field of visible light positioning, most of the localization is two-dimensional positioning under the condition of height determined. In addition, some three-dimensional visible light localization systems use a hybrid algorithm that greatly improves the computational complexity of the system, or requires the user to first provide a better initial point for three-dimensional positioning, which can’t be applied to life well. In order to solve the problems in the field of VLP, this paper proposes an indoor visible light positioning system based on improved chaotic particle swarm optimization. In this paper, the chaos algorithm is firstly used in the visible light positioning area. Meanwhile, the proposed algorithm combines chaos algorithm and particle swarm optimization algorithm, and obtains a high positioning accuracy in the simulation space of 3 m × 3 m × 4m. Chaos optimization algorithm can make use of visible light indoor positioning system to achieve the positioning accuracy greatly, and join the particle swarm algorithm can offset the slow convergence of the chaos algorithm. The simulation results show that the visible light positioning system based on CPSO algorithm can achieve the average error of less than 1.4 cm, and the positioning accuracy of 96.6% sampling points can reach within 3.55 cm.

Light positioning: A high-accuracy visible light indoor positioning system based on attitude identification and propagation model

With the booming development of green lighting technology, visible light-based indoor localization has attracted a lot of attention. Visible light-based indoor positioning technology leverages a light propagation model to pinpoint target location. Compared with the radio localization technology, visible light-based indoor positioning not only can achieve higher location accuracy, but also no electromagnetic interference. In this article, we propose LIPOS, a three-dimensional indoor positioning system based on attitude identification and visible light propagation model. The LIPOS system takes advantage of the existing lighting infrastructures to localize mobile devices that have light-sensing capabilities (e.g. a smartphone) using light emitting diode lamps as anchors. The system can accurately identify the attitude of a smartphone using its integrated sensors, distinguish different light emitting diode beacons using the fast Fourier transform algorithm, construct a position cost-function based on a visible light radiative decay model, and apply a nonlinear optimizing method to acquire the optimal estimation of final location. We have implemented the LIPOS system and evaluated it with a small-scale hardware testbed, as well as moderate-sized simulations. Extensive experiments are performed in three representative indoor environments—open-plan office, cubicle, and corridor, which not only demonstrate that the LIPOS can effectively avoid the negative effects of dynamic change of a smartphone’s attitude angle, but also show better locating accuracy and robustness, and obtain sub-meter level positioning accuracy.

Fast visible light indoor positioning system based on smart phone

Fast visible light indoor positioning system based on smart phone