Visible Light Positioning System Research Articles

Rate Optimization Based on Successive Convex Approximation Algorithm in the Self-powered Visible Light Communication and Positioning System

Rate Optimization Based on Successive Convex Approximation Algorithm in the Self-powered Visible Light Communication and Positioning System

Privacy preserving image sensor based visible light positioning receiver utilizing an ultra-flat singlet lens with high field-of-view and high aperture

Visible Light Positioning (VLP) is a promising candidate to enable widespread availability of location-based services in indoor environments. Existing VLP receiver designs achieve satisfactory accuracy, but often neglect cost-effectiveness and size constraints. To address this, we designed an ultra-flat singlet lens with a height of only 20 μm to be utilized in a compact image sensor-based VLP receiver. This specially designed lens brings the advantage of cost-effective manufacturing, high aperture, high field-of-view and short focal length. Compared to conventional cameras used in VLP systems, the presented optical receiver can be used in scenarios where privacy concerns play a critical role, like in wearable devices or in privacy-sensitive environments, as no perceivable image of the scene or the user is formed on the image sensor. With two separate VLP approaches, image processing based triangulation and machine learning assisted fingerprinting, we demonstrate positioning accuracy under 10 cm in an experimental setup. Furthermore, the demonstrator is integrated in an autonomously driving robot that has to avoid a geofence, exemplifying a location-based service.

Optimal design of integrated visible light communication and positioning system with energy harvesting.

In this paper, an optimization scheme that can simultaneously transmit communication information, positioning the information and energy in a visible light communication and positioning (VLCP) system with energy harvesting is proposed. The time switching-power splitting (TS-PS) method is applied, where the power and time allocation factors are defined as optimization variables, so that the system can maximize the harvested energy under the constraints of the information rate and positioning error. The multi-verse optimization (MVO) algorithm is introduced to obtain the optimal power and time allocation. In addition, the performance of the integrated system using the TS-PS method is investigated and compared with that using other conventional methods. The results show that a maximized harvested energy solution using the TS-PS method can harvest the most energy. Moreover, the effects of main external environment conditions, namely, the room height and field of view (FoV) of a photo diode (PD) on the system performance are also analyzed. The increase of the room height and FoV of the PD reduces the harvested energy, but does not change the information rate and positioning accuracy in the optimized system adopted in this paper.

A Visible Light 3D Positioning System for Underground Mines Based on Convolutional Neural Network Combining Inception Module and Attention Mechanism

To improve the accuracy of personnel positioning in underground coal mines, in this paper, we propose a convolutional neural network (CNN) three-dimensional (3D) visible light positioning (VLP) system based on the Inception-v2 module and efficient channel attention mechanism. The system consists of two LEDs and four photodetectors (PDs), with the four PDs on the miner’s helmet. Considering the height fluctuation of PD and the impact of wall reflection on the received light power, we adopt the Inception module to perform a multi-scale extraction of the features of the received light power, thus solving the limitation of the single-scale convolution kernel on the positioning accuracy. In order to focus on the information that is more critical to positioning among the numerous input features, giving different features of the optical power data corresponding weights, we use an efficient channel attention mechanism to make the positioning model more accurate. The simulation results show that the average positioning error of the system was 1.63 cm in the space of 6 m × 3 m × 3.6 m when both the line-of-sight (LOS) and non-line-of-sight (NLOS) links were considered, with 90% of the localization errors within 4.55 cm. During the experimental stage, the average positioning error was 11.12 cm, with 90% of the positioning errors within 28.75 cm. These show that the system could achieve centimeter-level positioning accuracy and meet the requirements for underground personnel positioning in coal mines.

Robustness enhancement of indoor visible light positioning system based on cascaded residual neural network

We proposed and experimentally demonstrated an ensemble learning scheme based on cascaded residual neural network (CRNN) to enhance the robustness of machine learning (ML) based visible light positioning (VLP) systems under model-shake. The training complexity of the CRNN is low. Experimental results reveal that with an acceptable complexity cost, the positioning accuracy under model-shake can be optimized from 8.31 cm to 2.04 cm and 1.56 cm with the proposed 2nd CRNN and 3rd CRNN, respectively.

Hybrid Positioning Algorithm for Tilted Receiver Using RSS and TDOA with Gaussian Process

In the visible light positioning (VLP) system, the received signal strength (RSS) algorithm has a better signal noise ratio performance than the time difference of arrival (TDOA) algorithm, while the RSS algorithm needs to work under the condition that the transmitter and receiver are strictly parallel. However, the receiver is prone to tilt due to environmental disturbances, which reduces the accuracy of the RSS algorithm. For the tilted receiver, the TDOA algorithm has a higher positioning accuracy than the RSS algorithm. In order to take full advantage of the two algorithms, we propose a hybrid positioning algorithm to locate the tilted receiver by using a Gaussian process (GP). The scheme separately uses RSS and the distance difference as the inputs of the GP model to estimate the position of the receiver. Then, according to the proposed positioning selection strategy, the more credible estimated position in our opinion is selected as the final estimated position. In addition, RSS information in the hybrid algorithm is extracted from the TDOA signal, which allows the hybrid algorithm to prevent an increase in the complexity of the VLP system. During the training and testing, RSS is normalized to meet the order-of-magnitude requirements of the GP model on the input data. Simulation results validate the hybrid algorithm based on a two-dimensional positioning system for the tilted receiver. When the standard deviations of the azimuth angle and elevation angle are 1°, the positioning accuracy of the hybrid algorithm is 53.7% higher than that of the RSS algorithm using an artificial neural network, and 49.9% higher than that of the RSS algorithm using a GP. The localization error under 1° standard deviations of azimuth and elevation angles is 20.2% lower than that under 20° standard deviations of the two angles.

Three-Dimensional NLOS VLP Based on a Luminance Distribution Model for Image Sensor

Visible light positioning (VLP) is playing a critical role in delivering better location-based services. However, traditional VLP systems rely on line-of-sight (LOS) paths and have a requirement for large numbers of light-emitting diodes (LEDs) and sensors, making them unprepared for various scenarios. This paper proposes a three-dimensional (3D) non-line-of-sight (NLOS) VLP system using a single LED and an image sensor (IS) to address the problem of obstructed LOS paths. On the one hand, an optical camera communication (OCC) subsystem is designed to receive the transmitter’s position based on NLOS links. In contrast, two highlights are visible in the picture when the image sensor captures the reflected light from the floor. Using the proposed luminance distribution model (LDM), it can be demonstrated that the two highlights can be regarded as the projections formed by two virtual LEDs. Based on the projection equations of the two virtual LEDs, an image sensing algorithm can estimate the receiver’s position. Experimental results show that the proposed system can achieve a 90th percentile accuracy of < 22 cm for 3D positioning. To the best of author’s knowledge, this is the first work to demonstrate 3D NLOS VLP using just a single LED and an IS. Additionally, the proposed LDM is the first physically based model for the first reflected light to estimate the channel gain of a NLOS link.

On the time–frequency-based two-light-emitting diode positioning in visible light communication

Conventional visible light positioning (VLP) schemes usually require at least three light sources, making positioning accuracy sensitive to shadowing, a limited number of light sources, or sparse light-emitting diode (LED) layout. We proposed a mirror-assisted VLP system, where two-LED positioning can be realized by adopting specular reflection and time–frequency combination method. The scheme is based on the concept of virtual lamps and the received signal strength algorithm. Specular reflection power from one of the virtual lamps is integrated into the effective positioning power of its corresponding real lamp to obtain increased received power and mitigate the impact of diffuse reflection. Meanwhile, the other virtual lamp is independently used as the third light source for positioning, which means that the system becomes a “pseudo” three-LED positioning system, so that positioning can be realized with only two real lamps available. The corresponding positioning accuracy evaluated numerically shows an upward trend as the distance between the receiver and mirror decreases. By reducing the distance between the transmitters and the mirror, the positioning error can be limited to ∼12.4 cm.

Multi-User Visible Light Communication and Positioning System Based on Dual-Domain Multiplexing Scheme

Visible light communication and positioning (VLCP) is a promising candidate for constructing a multi-functional wireless network with large-scale connectivity and centimeter-level positioning. However, there is still a lack of effective methods to offer simultaneous visible light communication (VLC) and visible light positioning (VLP) functions for multiple users. Thus, we propose a multi-user VLCP system based on a dual-domain multiplexing (DDM) scheme, where both the time and code resources are multiplexed to transmit VLCP signals for multiple users simultaneously. In the proposed system, the data of different users are distinguished by using code division multiplexing technology, while the VLCP signals transmitted from different LEDs are separated by adopting time division multiplexing technology. The performances, including bit-error rate and positioning error, are evaluated through both simulation and experimentation to verify the feasibility of the proposed multi-user VLCP system. In the experiment, a VLCP system with four LED transmitters was able to simultaneously support low-speed VLC with free error and accurate VLP with a 2 cm precision for eight users. This offers an effective solution to support a large number of users with simultaneous VLC and VLP functions in the future multi-functional wireless network.

Recent Progress in Visible Light Positioning and Communication Systems

In this paper, we report the recent progress in visible light positioning and communication systems using light-emitting diodes (LEDs). Due to the wide deployment of LEDs for indoor illumination, visible light positioning (VLP) and visible light communication (VLC) using existing LEDs fixtures have attracted great attention in recent years. Here, we review our recent works on visible light positioning and communication, including image sensor-based VLP, photodetector-based VLP, integrated VLC and VLP (VLCP) systems, and heterogeneous radio frequency (RF) and VLC (RF/VLC) systems.

Single LED visible light positioning system based on image sensor and calculated azimuth angle.

A single-lamp visible light positioning (VLP) system based on an optical camera is proposed and demonstrated. The system uses a circular light-emitting diode (LED) to transmit ID data, and a common smartphone camera for positioning. First, we propose a scheme to calculate the azimuth angle using a marked lamp bead, avoiding the use of a magnetometer, which can improve the accuracy of the azimuth angle. Second, we propose a new positioning method using a chord in the image plane, which passes through the midpoints of two chords parallel to the LED plane. The experimental results show that when the experimental area is 1.8m×3.6m, the three-dimensional (3D) average positioning errors are 8.0, 7.5, and 7.2cm for the heights of 1.75, 1.45, and 1.2m.

Indoor high-precision visible light positioning system using Jaya algorithm.

Several indoor positioning systems that utilize visible light communication (VLC) have recently been developed. Due to the simple implementation and high precision, most of these systems are dependent on received signal strength (RSS). The position of the receiver can be estimated according to the positioning principle of the RSS. To improve positioning precision, an indoor three-dimensional (3D) visible light positioning (VLP) system with the Jaya algorithm is proposed. In contrast to other positioning algorithms, the Jaya algorithm has a simple structure with only one phase and achieves high accuracy without controlling the parameter settings. The simulation results show that an average error of 1.06 cm is achieved using the Jaya algorithm in 3D indoor positioning. The average errors of 3D positioning using the Harris Hawks optimization algorithm (HHO), ant colony algorithm with an area-based optimization model (ACO-ABOM), and modified artificial fish swam algorithm (MAFSA) are 2.21 cm, 1.86 cm and 1.56 cm, respectively. Furthermore, simulation experiments are performed in motion scenes, where a high-precision positioning error of 0.84 cm is achieved. The proposed algorithm is an efficient method for indoor localization and outperforms other indoor positioning algorithms.

Mechanism of Minimum Frequency Resolution in a Visible Light-Positioning System

Mechanism of Minimum Frequency Resolution in a Visible Light-Positioning System

Indoor Visible Light Positioning Based on Improved Whale Optimization Method With Min-Max Algorithm

Recently, the Min-Max algorithm has been widely used in various indoor positioning systems due to its simplicity and robustness. In this article, an improved whale optimization algorithm (IWOA) associated with the Min-Max algorithm is proposed to improve the positioning accuracy concerning the original Min-Max algorithm for visible light positioning (VLP) systems. In the proposed algorithm, the three-dimensional (3-D) region of interest (RoI) affiliated with the target node (TN) is first obtained using the original Min-Max algorithm, and then the IWOA is employed to find the accurate position of the TN in the 3-D RoI with the lowest fitness value. In software simulations, the signal-to-noise ratio (SNR) value of the VLP system is 15 dB and the standard deviation of measured distance noise is 1 m. Our software simulation results show that in the case of two-dimensional (2-D) positioning, the averaged positioning error (APE) of the proposed IWOA-Min-Max algorithm is decreased by 84.6%, 52.1%, 1.9%, and 19.93%, respectively, for the original Min-Max algorithm, the extended Min-Max algorithms, the whale optimization algorithm (WOA), and the least square estimation (LSE). Furthermore, in the case of 3-D positioning, it is also reduced by 81.7%, 75.4%, 8.5%, and 63.9%, respectively, compared with that of the above four existing algorithms. Finally, hardware-in-the-loop simulation (HILS) results are also provided to verify the effectiveness of the proposed IWOA-Min-Max algorithm.

VWR-SLAM: Tightly Coupled SLAM System Based on Visible Light Positioning Landmark, Wheel Odometer, and RGB-D Camera

Visible light positioning (VLP) is a promising technology since it can provide high-accuracy indoor localization based on the existing lighting infrastructure. Most VLP systems require a prior light-emitting diode (LED) location map, termed a VLP-landmark map in this article, for which manual surveys are costly in practical deployment at scale. What is more, the existing approaches also require dense LED deployments. In this work, we proposed a multisensor fusion framework, termed VWR-simultaneous localization and mapping (SLAM), which tightly fused the VLP, wheel odometer, and red green dlue-depth map (RGB-D) camera to achieve SLAM. Our VWR-SLAM can provide accurate and robust robot localization and navigation in LED shortage/outage situations, meanwhile, constructing the 3-D sparse environment map and the 3-D VLP-landmark map without tedious manual measurements. The experimental results show that our proposed scheme can provide an average robot positioning accuracy of 1.81 cm and an LED mapping accuracy of 3.01 cm.

Accurate visible light positioning technique using extreme learning machine and meta-heuristic algorithm

Since input weights and hidden biases affect the overall performance of extreme learning machine (ELM) based on a single-hidden layer, two meta-heuristic algorithms of grey wolf optimizer (GWO) and particle swarm optimization (PSO) are adopted to optimize the input weights and hidden biases of ELM respectively in order to obtain higher classification accuracy. Namely, ELM-GWO and ELM-PSO are two enhanced ELM algorithms. Compared with the other existing techniques such as the traditional ELM and adaptive boosting (AdaBoost), the positioning performance of two enhanced ELM algorithms is analyzed by simulation and experiment in visible light positioning (VLP) systems. The experimental results show that the probabilities of two-dimensional (2D) positioning error being less than 10 cm for ELM, ELM-GWO, ELM-PSO, and AdaBoost are 84.25%, 90.25%, 93.25%, and 19.75%, respectively. And the probabilities of three-dimensional (3D) positioning error being less than 10 cm for ELM, ELM-GWO, ELM-PSO, and AdaBoost are 45.17%, 84.67%, 80.33%, and 16%, respectively. Both simulation and experimental results show that two enhanced ELM algorithms have better positioning performance and robustness. In addition, although increasing the number of iterations and the number of hidden neurons can effectively reduce the positioning error, the computational complexity is relatively high in large-scale fingerprint positioning scenarios. Therefore, a hybrid positioning method combining enhanced ELM and region division is further proposed. Both simulation and experimental results show that the proposed hybrid method can significantly improve the positioning accuracy while reducing the computational complexity.

Utilizing Lighting Design Software for Simulation and Planning of Machine Learning Based Angle-of-Arrival (AOA) Visible Light Positioning (VLP) Systems

We propose to utilize a commercially available DIALux lighting design software for simulation and planning of machine learning (ML) based angle-of-arrival (AOA) visible light positioning (VLP) systems. Here, different ML models, for example, second order linear regression (LR), artificial neural-network (ANN), and convolutional neural-network (CNN) are employed. The proposed VLP simulator works well with different ML algorithms. The results show that the proposed scheme can acts as an effective indoor VLP planning and design tool. Besides, it may also alleviate the training data collection in ML based VLP systems.

Beacon LED Coordinates Estimator for Easy Deployment of Visible Light Positioning Systems

Traditional visible light positioning (VLP) systems estimate receivers’ coordinates based on the assumption that light-emitting diode (LED) coordinates are known accurately, which is not always practical. Even with accurate laser range finders, the structural changes of the buildings due to temperature, humidity, and material aging might affect the LED coordinates unpredictably. Besides, when the number of LEDs increases, it is time-consuming to build a database of LED coordinates. In this paper, we propose a novel system exploiting two optical angle-of-arrival (AOA) estimators to localize the LEDs, and the AOA estimators have fixed relative positions. Each AOA estimator has four photodiodes (PDs) towards different orientations to estimate the incident light direction. Considering the additive noises imposed on the PDs, we derive a closed-form error expression of the LED coordinate estimator. Analytical and simulation results show that the two AOA estimators should be set symmetrically with respect to the room center to reduce the error of the LED coordinates database. Moreover, an experimental platform is built with two fixed AOA estimators and one moving LED in order to verify the approach. The measured average localization error is 4.46 cm and the time of one localization is about 23.4 ms.

3D indoor positioning with spatial modulation for visible light communications

In this paper, a novel three-dimensional (3D) indoor visible light positioning (VLP) algorithm is proposed based on the spatial modulation (SM) and its error performance assessed as compared to the conventional received signal strength (RSS)-based 3D VLP systems. As contrasted to the traditional VLP system, the proposed SM-based 3D VLP system first estimates the optical channel gain between the transmitting light-emitting diodes (LEDs) and the two photo detectors (PDs) attached to the user by a pilot-based channel estimation technique. Then, unknown 3D positions of the receiver are determined by the trilateration algorithm with distances computed from the estimates of the channel gains. Consequently, the 3D VLP system achieves an interference-free transmission with increased spectral efficiency and without the need for a demultiplexing process at the receiving end. The algorithm’s performance is evaluated regarding positioning error by applying the SM over four LEDs and the number of pilots selected as a function of the environmental signal-to-noise ratios (SNRs). The computer simulation results show that the positioning errors are obtained in an order of magnitude smaller than RSS-based techniques in an indoor industrial environment. This is mainly because the distances involved in determining the 3D positions can be determined more precisely by the pilot-aided channel estimation method without creating any data rate problem in transmission due to the higher spectral efficiency of the SM.

Low-Complexity Visible Light Positioning and Rotation Estimation Based on Eigenvalue Decomposition

Most existing visible light positioning (VLP) systems assume vertically placed receivers, or estimate the receiver rotations through gyroscopes. Although several recent VLP systems can deal with the arbitrary tilting of the receiver without the additional sensors, the positioning frequency, algorithm efficiency and stability are still to be improved. In this work, we propose a novel positioning and rotation estimation algorithm based on the eigenvalue decomposition of the covariance matrix of light direction vectors. Four or more light-emitting diodes (LEDs) serve as the anchors and three or more tilted photodiodes are equipped on the mobile receiver. The new algorithm's computational complexity only grows linearly with the number of LEDs, and it can distinguish and bypass the local optima by verifying if the objective function reaches its theoretical maximum. We use simulations to reveal the error patterns of the algorithm and build a low-cost prototype to verify its real performance. The measured average positioning error is 1.83 cm and the orientation estimation error is 0.04 rad. The proposed scheme can be used for positioning and navigation that require fast response, low power consumption, low cost, high accuracy and small device size.