Indoor Visible Light Positioning Research Articles

Efficient three-dimensional indoor dark light visible light positioning with received signal strength technique

A unidirectional Visible Light Communication (VLC) system has the potential to serve as an Indoor Positioning System (IPS). However, it is important to note that many existing VLC IPS setups rely on keeping LED lights switched on, which can lead to energy wastage when indoor lighting is unnecessary during daylight hours. Additionally, most indoor Visible Light Positioning (VLP) research has focused on 2D localization, assuming a fixed receiver height. This approach tends to overlook the positional inaccuracies that can arise due to variations in height. In this paper, to address these concerns, a 3D IPS is proposed. This system employs multiple transmitters and a single receiver, utilizing a Dark Light Visible Light Communication (DL-VLC) system. This innovative approach ensures the functionality of IPSs even when LEDs are turned "OFF" resulting in more efficient energy usage. The results of the investigation indicate that the positioning error remains within approximately 3 cm when the receiver is positioned at a height of 2.4 m, assuming standard room dimensions, which is in a fair agreement with previously published work.

Reduced resource competition based on mCAP and FD‐OZCZ code for VLCP system

AbstractThe advancement of smart cities and intelligent industries has accentuated the necessity for reliable indoor communication and precise indoor positioning. In response, this paper presents an innovative integrated system for indoor visible light communication and positioning, designed to facilitate robust communication and pinpoint accuracy in positioning for indoor applications. We incorporate multiband carrierless amplitude and phase (CAP) modulation to mitigate interference between communication and positioning subcarriers. Unlike the conventional orthogonal frequency division multiplexing‐based subcarrier modulation (OFDM‐SCM), CAP demonstrates reduced out‐of‐band interference and a diminished peak‐to‐average power ratio (PAPR). Moreover, we introduce the frequency domain optical zero correlation zone (FD‐OZCZ) code, integrated within the CAP signal to optimize the distribution of positioning subcarriers. Simulation outcomes substantiate that our proposed CAP, augmented with FD‐OZCZ, outperforms OFDM‐SCM systems in delivering reliable communication and exceptional positioning accuracy.

Optical Camera Communications and Machine Learning for Indoor Visible Light Positioning

The paper was withdrawn by author's request.

An Improved Cuckoo Search Algorithm Based on Elite Opposition-based Learning for Indoor Visible Light Positioning

<p>In the cuckoo search algorithm, the structure is simple, and the parameters are not much, but it is easy to trap into the local optimum, and in the later period, the convergence speed is plodding. Aiming at the shortcomings of the standard cuckoo algorithm, a modified cuckoo algorithm (EACSDAM) is presented in this paper, which adopts elite reverse learning to enhance the population diversity, and increases the step factor and discovery probability to improve the global detection and local searchability. Eight standard test functions are used to simulate the EACSDAM algorithm. Compared with the standard cuckoo algorithm and the other two improved algorithms, the accuracy and convergence speed of EACSDAM are greatly improved. In the end, EACSDAM is used to optimize the indoor 3D visible light positioning. The simulation results indicate that EACSDAM has a more powerful ability for global optimization, and more accurate positioning, and the positioning error is significantly reduced.</p> <p> </p>

Indoor Visible Light Positioning Based on Channel Estimation and Cramér–Rao Low Bound Analysis with Random Receiving Orientation of User Equipment

The accuracy of line-of-sight (LOS)-based indoor visible light positioning (VLP) depends directly on the accuracy of the received LOS power. In an actual indoor visible light communication (VLC) system, the total received power contains the LOS power and diffuse power, and the proportion of diffuse component varies significantly with the receiving orientation of the user equipment (UE). In the trilateration positioning process, this is bound to bring a very high level of inaccuracy for at least one or two of them when determining the distance between the positioned LED and UE. To solve this problem, we employed channel estimation and Parseval’s theorem to remove the diffuse component from the total received power. The simulation results show that the higher the channel estimation accuracy, the smaller the discrepancy between the estimated and actual LOS power, and the greater the localization accuracy. At the same channel estimation accuracy, the comparison results demonstrate that the localization performance of the proposed method is higher than LLS-1, in which the total received power is directly adopted to estimate the distance. The localization performance of LLS-2 deteriorates significantly compared to the proposed method. This result indicates that the receiving orientation cannot be neglected during the distance estimation process even though the estimated accuracy of LOS power is high. Deriving the Cramér–Rao low bound (CRLB) for LOS-based indoor VLP with receiving orientation, we compared the localization performance for the proposed method with LLS-1, LLS-2, and the CRLB at various signal-to-noise ratios (SNRs). The results show that the localization error of all the received positions in the room can reach the CRLB at approximately 25 dB. However, localization errors based on LLS-1 and LLS-2 cannot achieve the theoretical CRLB even at a high SNR.

Four-dimensional indoor visible light positioning: A deep-learning-based perspective

Accurate indoor positioning has been a difficult problem as the last meter dilemma of localization and navigation due to lack of satellite navigation signals and complex characteristics of multipath and dynamic indoor channels. Indoor visible light positioning (VLP) provides a new possible paradigm for accurate and low-complexity indoor positioning using widely deployed light-emitting diodes (LED). In this paper, the received signal strength of the photodetector at a mobile terminal is utilized to extract the geometric features in order to infer the accurate position coordinate via deep learning. Specifically, a hybrid model, i.e., a convolutional-recurrent neural network (CRNN), is devised to learn the nonlinear mapping from the received signal strength to the position coordinates in the complex indoor visible light propagation environment. A four-dimensional (4D) VLP architecture based on CRNN is formulated to deal with the non-line-of-sight propagation of indoor visible light and different receiver orientation. Simulation results show that the proposed CRNN-based 4D VLP (CR4D-VLP) method can achieve centimeter-level positioning accuracy, and significantly outperforms other state-of-the-art deep-learning-based schemes in both line-of-sight and non-line-of-sight scenarios with various spatial patterns of LEDs and different room sizes.

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.

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.

RSS-based visible light positioning with unknown receiver tilting angle: robust design and experimental demonstration.

In this paper, we consider the received signal strength (RSS)-based indoor visible light positioning (VLP) with unknown receiving angle. A novel VLP scheme is proposed by joint estimation of the receiver coordinate and receiver characteristic vector. We equivalently convert the original estimation problem into the problem that maximizes the projection of the RSS vector on the column space spanned by the measurement matrix. The proposed scheme does not require the prior knowledge of receiving coefficient and receiving angle. To further reduce the computational complexity, we also propose an importance sampling (IS) method for solving the VLP problem. Simulation and experimental results demonstrate the robustness of the proposed scheme to the receiving coefficient and receiving angle. Specifically, experimental results show that the proposed VLP scheme achieves a stable positioning accuracy below 7 cm under different receiver tilting angles in a 60 cm × 60 cm × 150 cm space, and is not sensitive to height perturbation.

Using lighting design tool to simplify the visible light positioning plan and reduce the deep learning loading.

We put forward and transform the commercially available lighting design software into an indoor visible light positioning (VLP) design tool. The proposed scheme can work well with different deep learning methods for reducing the loading of training data set collection. The indoor VLP models under evaluation include second order regression, fully-connected neural-network (FC-NN), and convolutional neural-network (CNN). Experimental results show that the similar positioning accuracy can be obtained when the indoor VLP models are trained with experimentally acquired data set or trained with software obtained data set. Hence, the proposed method can reduce the training loading for the indoor VLP.

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.

The Usage of ANN for Regression Analysis in Visible Light Positioning Systems.

In this paper, we study the design aspects of an indoor visible light positioning (VLP) system that uses an artificial neural network (ANN) for positioning estimation by considering a multipath channel. Previous results usually rely on the simplistic line of sight model with limited validity. The study considers the influence of noise as a performance indicator for the comparison between different design approaches. Three different ANN algorithms are considered, including Levenberg–Marquardt, Bayesian regularization, and scaled conjugate gradient algorithms, to minimize the positioning error () in the VLP system. The ANN design is optimized based on the number of neurons in the hidden layers, the number of training epochs, and the size of the training set. It is shown that, the ANN with Bayesian regularization outperforms the traditional received signal strength (RSS) technique using the non-linear least square estimation for all values of signal to noise ratio (SNR). Furthermore, in the inner region, which includes the area of the receiving plane within the transmitters, the positioning accuracy is improved by 43, 55, and 50% for the SNR of 10, 20, and 30 dB, respectively. In the outer region, which is the remaining area within the room, the positioning accuracy is improved by 57, 32, and 6% for the SNR of 10, 20, and 30 dB, respectively. Moreover, we also analyze the impact of different training dataset sizes in ANN, and we show that it is possible to achieve a minimum of 2 cm for 30 dB of SNR using a random selection scheme. Finally, it is observed that is low even for lower values of SNR, i.e., values are 2, 11, and 44 cm for the SNR of 30, 20, and 10 dB, respectively.

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.

High accuracy indoor visible light positioning using a long short term memory-fully connected network based algorithm

In this work, a novel positioning algorithm based on a long short term memory-fully connected network (LSTM-FCN) is proposed to improve the performance of an indoor visible light positioning (VLP) system. Using the proposed LSTM-FCN based positioning algorithm, the VLP system with a single light emitting diode (LED) and multiple photodetectors (PDs) was implemented. On this basis, the positioning performance of the established VLP system using proposed LSTM-FCN, traditional FCN and support vector regression (SVR) based algorithm is investigated and compared. It is demonstrated that the VLP system using the proposed LSTM-FCN based algorithm has better performance than that using other machine learning algorithms. As a result, an average positioning error of 0.92 cm and a maximum positioning error of less than 5 cm can be obtained for the established VLP system.

Optimal pulse design for visible light positioning systems

The problem of optimal pulse design for light-emitting diode (LED) transmitters is investigated in an indoor visible light positioning (VLP) setup. In particular, the problem of localization performance maximization is formulated for both asynchronous and synchronous VLP systems with consideration of practical limitations related to power consumption, illumination levels, and/or effective bandwidths, while quantifying the localization accuracy via the Cramér–Rao lower bound (CRLB). In both asynchronous and synchronous scenarios, the formulated problems are shown to be convex optimization problems, and some properties of the optimal solutions are derived. In addition, the pulse design problem for minimum power consumption is formulated under a CRLB constraint along with other practical limitations; and this problem is also revealed to be a convex optimization problem. Based on the solutions of the proposed optimization problems, pulse design procedures are described to determine the parameters of optimal pulse shapes. Numerical results illustrate the benefits of the proposed optimal pulse design approach in comparison with the state-of-the-art optimal power allocation scheme in the literature. In particular, electrical power consumption can be reduced by around 45% or localization accuracy can be improved by as much as 25% via the proposed optimal pulse design approach in certain scenarios.

Experimental investigation of environmental interference mitigation and blocked LEDs using a memory-artificial neural network in 3D indoor visible light positioning systems.

Environmental interference and blocked light-emitting diodes (LEDs) often happen in the received signal strength (RSS)-based indoor visible light positioning (VLP) systems, while few solutions to these problems exist. In this paper, we proposed a novel deviation-correction algorithm named memory-artificial neural network (M-ANN) in the 3-dimensional (3D) indoor RSS-VLP system. By memorizing and utilizing the features of signal strength conversion between adjacent test moments, M-ANN can adapt to different test environments in the positioning process. Also, with the help of a designed genetic algorithm (GA) module, M-ANN can efficiently search and retrieve the missing data from an offline simulation database to prevent the VLP outage caused by the blocked LED. The experimental results in a test region of 0.6×0.6×0.8 m3 demonstrate that the proposed M-ANN can significantly mitigate the impact of environmental interference, and it can still maintain relatively high-precision positioning even in the case of blocked LEDs. The average positioning error of 1.04 cm, 2.89 cm, and 3.53 cm is experimentally achieved in the situation of environmental interference, one blocked LED and two blocked LEDs, respectively.

A High-Coverage Camera Assisted Received Signal Strength Ratio Algorithm for Indoor Visible Light Positioning

A high-coverage algorithm termed enhanced camera assisted received signal strength ratio (eCA-RSSR) positioning algorithm is proposed for visible light positioning (VLP) systems. The basic idea of eCA-RSSR is to utilize visual information captured by the camera to estimate first the incidence angles of visible lights. Based on the incidence angles, eCA-RSSR utilizes the received signal strength ratio (RSSR) calculated by the photodiode (PD) to estimate the ratios of the distances between the LEDs and the receiver. Based on an Euclidean plane geometry theorem, eCA-RSSR transforms the ratios of the distances into the absolute values. In this way, eCA-RSSR only requires three LEDs for both orientation-free 2D and 3D positioning, implying that eCA-RSSR can achieve high coverage. Based on the absolute values of the distances, the linear least square method is employed to estimate the position of the receiver. Therefore, for the receiver having a small distance between the PD and the camera, the accuracy of eCA-RSSR does not depend on the starting values of the non-linear least square method and the complexity of eCA-RSSR is low. Furthermore, since the distance between the PD and camera can significantly affect the performance of eCA-RSSR, we further propose a compensation algorithm for eCA-RSSR based on the single-view geometry. Experiment results show that positioning errors of less than five centimeters is achievable for eCA-RSSR. Simulation results show that eCA-RSSR can achieve 80th percentile accuracy of about four centimeters and can improve the coverage ratio at low cost.

Three-Dimensional Indoor Visible Light Positioning with a Tilt Receiver and a High Efficient LED-ID

Nowadays, indoor visible light positioning (VLP) is one of the hottest technologies in the field of positioning; as a result, a number of algorithms for VLP have been proposed. However, unfortunately, few algorithms can be applied to the case that the receiver is tilted. In order to solve this problem, we developed a three-dimensional indoor VLP algorithm which can work effectively and accurately when the lens is tilted. When the lens is far away from the LED, the image of the LED is approximately regarded as an ellipse, and the distance between the two is obtained through the geometric relationship, and we finally achieve the positioning of the camera by the triangulation algorithm. The quantitative results show an average error of 6.74 cm when the tilt angle is estimated to be within 30°. At the same time, we propose an ID allocation scheme, which can effectively reduce the demand for ID.

Employing DIALux to relieve machine-learning training data collection when designing indoor positioning systems.

We propose and demonstrate using the DIALux software with our proposed linear-regression machine-learning (LRML) algorithm for designing a practical indoor visible light positioning (VLP) system. Experimental results reveal that the average position errors and error distributions of the model trained via the DIALux simulation and trained via the experimental data match with each other. This implies that the training data can be generated in DIALux if the room dimensions and LED luminary parameters are available. The proposed scheme could relieve the burden of training data collection in VLP systems.