Visible Light Positioning Research Articles

Indoor tracking for smart hospital by a hybrid visible light positioning system

<p class="Abstract">Modern hospital operations and management, i.e., smart hospitals, require pervasive system-wide wireless communication and positioning capabilities. Unfortunately, this cannot be entirely facilitated by existing radio frequency (RF) wireless technologies due to interference concerns in hospitals. We design and implemented a novel hybrid RF-free wireless tracking system utilizing combined visible light communication (VLC) and positioning (VLP), and powerline communication (PLC) technologies to enable future smart hospital operations. This VLC/VLP/PLC tracking system consists of host optical transceivers embedded in light-emitting diode (LED) bulbs and user-end photodetector (PD) optical tags, as well as backbone powerline interface, to form a ubiquitous wireless tracking and communication network connecting all people and equipment throughout all hospital buildings. The new indoor optical-based tracking system was validated via simulation and experiments. Being embedded into the existing LED lighting infrastructure, this hybrid VLC/VLP/PLC tracking system technology is accurate, ultralow cost and environment friendly. Smart hospitals will lead to efficient and affordable healthcare, a revolutionary solution in the human society.</p>

Waveform Design and Optimization for Integrated Visible Light Positioning and Communication

In this paper, we investigate an energy efficient waveform design for integrated visible light positioning and communication (VLPC) systems by exploiting the relationship between visible light positioning (VLP) and visible light communication (VLC). We propose that the direct current component and the alternating current component of the VLPC signals are utilized for positioning and communication, respectively. With a single LED-lamp, we propose a received-signal-strength based 3D VLP scheme, and further derive the Cramer-Rao lower bound (CRLB). Then, by exploiting the inherent coupling relationship between VLP and VLC, the positioning results are utilized for channel estimation of VLC, which can significantly reduce the channel estimation pilot overhead. Furthermore, we optimize the waveform design by minimizing the CRLB, while satisfying both the outage probability of communication rate and total transmit power constraints. However, this problem turns to be non-convex and intractable. To address this challenging problem, we utilize the Conditional Value-at-Risk to conservatively transform the outage probability constraint into a deterministic form. By exploiting the block coordinate descent algorithm, the waveform design problem can be efficiently solved by alternately optimizing VLP and VLC convex sub-problems and dual problem. Finally, simulation results verify both the effectiveness and robustness of the proposed waveform design.

Indoor High Precision Positioning System Based on Visible Light Communication and Location Fingerprinting

Indoor positioning system based on visible light communication and location fingerprinting can achieve cm-level positioning accuracy, which has become an important candidate for high precision positioning. However, in the case of non-uniform and low-density location fingerprinting distribution, it will still lead to large errors and poor robustness. To solve this problem, an accurate visible light positioning (VLP) method based on location fingerprinting and meta-heuristic is proposed, and all possible cases of uneven distribution of location fingerprinting are considered by introducing the random selection rate. The effectiveness of the proposed VLP method is compared with other six VLP methods based on location fingerprinting. Simulation results show that when the signal-to-noise ratio is 20 dB, the average positioning error of the proposed VLP method is 0.51 cm, which is at least 80.23% lower than the other six VLP methods. Experimental results show that when the random selection rate is 50%, the average positioning error of the proposed VLP method is 3.11 cm, which is at least 41.87% lower than the other six VLP methods. The proposed VLP method can be applied to positioning scenarios with different location fingerprinting distributions. The research results can provide a new research idea for the fingerprinting weight calculation method.

Diffuse Reflection Effects in Visible Light Positioning: Analysis, Modeling, and Evaluation

Currently, the Global Positioning System (GPS) is widely used, but its signal is attenuated by factors such as trees, walls, and ceilings, which severely degrade its positioning accuracy. To fill the gap, various indoor positioning techniques have attracted increasing attention in recent years. Visible light positioning (VLP) is a promising scheme for indoor positioning due to its high precision, high security, and low energy consumption; however, ubiquitous diffuse reflection affects the accuracy and robustness of VLP. During our testing, we found that diffuse reflection could increase the error in RSS values by 20~30%, severely affecting VLP accuracy; however, diffuse reflection is inevitable in real positioning environments. To solve this problem, this paper first establishes a wall diffuse reflection model and then implements a visible light positioning system based on an Internet of Things platform. Finally, this paper uses the system to verify the effectiveness of the diffuse reflection model. The experiments show that the proposed model effectively improves positioning accuracy by 36.7~61.3%.

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.

Robust Power Allocation for Integrated Visible Light Positioning and Communication Networks

Integrated visible light positioning and communication (VLPC), capable of combining advantages of visible light communications (VLC) and visible light positioning (VLP), is a promising key technology for the future Internet of Things. In VLPC networks, positioning and communications are inherently coupled, which has not been sufficiently explored in the literature. We propose a robust power allocation scheme for integrated VLPC Networks by exploiting the intrinsic relationship between positioning and communications. Specifically, we derive explicit relationships between random positioning errors, following both a Gaussian distribution and an arbitrary distribution, and channel state information errors. Then, we minimize the Cramer-Rao lower bound (CRLB) of positioning errors, subject to the rate outage constraint and the power constraints, which is a chance-constrained optimization problem and generally computationally intractable. To circumvent the nonconvex challenge, we conservatively transform the chance constraints to deterministic forms by using the Bernstein-type inequality and the conditional value-at-risk for the Gaussian and arbitrary distributed positioning errors, respectively, and then approximate them as convex semidefinite programs. Finally, simulation results verify the robustness and effectiveness of our proposed integrated VLPC design schemes.

Advances in Indoor Localization: Comparative Study of RFID, Wi-Fi, and Visible Light Methods

Due to the limitation that the global positioning system cannot be used in indoor settings and the increasing demand for location-based services in indoor environments, indoor localization has become one of the hottest research topics in recent years. A variety of techniques have been proposed for indoor localization. This review paper aims to provide an overview of working principles of radio frequency identification based, Wi-Fi based and visible light localization techniques and their applications. The paper also presents a comprehensive analysis of the advantages and disadvantages of each technique, including their accuracy, cost-effectiveness, and ease of deployment. In the end, the paper predicts a few future research directions and potential opportunities for indoor localization. This paper will be of interest to researchers, practitioners, and industry professionals working on indoor localization and related fields.

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.

Enhanced Pedestrian Navigation on Smartphones With VLP-Assisted PDR Integration

Pedestrian dead-reckoning (PDR) is a potential indoor localization technology that utilizes the inertial measurement unit (IMU) to estimate location. However, one of its most significant drawbacks is the accumulation measurement error. This paper proposes a PDR system integrated with visible light positioning (VLP) that boasts low accumulated error and can achieve real-time and accurate indoor positioning using both the IMU and the camera sensor on a smartphone. A multi-frame fusion method for LED-ID decoding is introduced in the encoding and decoding process of the system, achieving 98.5% decoding accuracy with a 20-bit-long LED-ID at the height of 2.1 m. This method also accommodates the variation in the shutter speeds of cameras and heights of the LED. Meanwhile, absolute locations and step length could be further calibrated using a single light-emitting diode (LED), providing an average accuracy within 0.5 meters over a 108-meter walk.

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.

Visible light fingerprint database recovery algorithm based on CP decomposition.

Visible light communication(VLC) is a new method of indoor communication. It can provide an effective solution for indoor positioning. Fingerprint-based visible light positioning(VLP) has been widely studied for its feasibility and high accuracy. The acquisition of 'fingerprint database' is crucial for accurate VLP. However, sparse sensors such as photodiode(PD) can only be arranged because of the space-limited scenario and high costs. Correspondingly, it results in the loss of the fingerprint database. Therefore, it is indispensable to solve the problem of how to effectively and accurately recover the fingerprint database from measurements of sparsely arranged sensors. In this paper, we propose a spatio-temporal constraint tensor completion (SCTC) algorithm based on CANDECOMP/PARAFAC (CP) decomposition to recover the fingerprint database from measurements of sparsely arranged sensors. Specifically, we model the measurements from the spatial and temporal dimensions as a tensor, and formulate the optimization problem based on the low-rank feature of the tensor. To improve the recovery accuracy, spatial and temporal constraint matrices are introduced to effectively constrain the optimization direction when completing the tensor. Spatial constraint matrices are constructed by utilizing the mode-n expansion matrix of the tensor based on the undirected graph theory. Accordingly, the Toeplitz matrix is used as the temporal constraint matrix to excavate the temporal correlation of the tensor. Since the optimization problem is non-convex and difficult to solve, we introduce CP decomposition to decompose the tensor into several factor matrices. By solving the factor matrices, the original tensor is reconstructed. The performance of the proposed SCTC algorithm is confirmed via experimental measured data.

Indoor receiving signal strength based visible light positioning enabled with equivalent virtual lamps.

Visible light positioning (VLP) has become one of the most potent alternative methods in indoor positioning scenarios. However, the system performance of VLP is largely related to the characteristics of the wireless optical channel. Limited access resulting from shadowing, obstacles, and multipath propagation between transmitters and photodetectors might lead to positioning failure. In this paper, we propose a mirror-assisted VLP method based on the received signal strength algorithm. By introducing specular reflection into the system model, multipath propagation can be effectively alleviated, making it possible to implement VLP with limited accessibility of LEDs. Simulation results show that the proposed approach, based on the assumption of equivalent virtual lamps, can mitigate the non-line-of-sight interference in a classical four-lamp room. Average positioning performance improves by 25% with the assistance of a mirror. In addition, trilateration positioning can be realized when only two light-emitting diodes are available in the target room. The positioning accuracy of the two-LED VLP can reach ∼30c m under scenarios considering non-line-of-sight reflections.

Tilted-angle insensitive received signal strength in visible light positioning systems using a deep neural network trained by synthetic data

Despite extensive research on received signal strength (RSS)-based visible light positioning (VLP), the receiver (RX) is assumed to stand vertically during the positioning process in most reported system designs. In this work, we propose a positioning strategy using a deep neural network (DNN) trained by synthetic data to address this problem. We further explicitly state the deficiencies in the current RSS-VLP algorithms when handling positioning problems involving RX orientation. Compared with existing RSS-VLP algorithms, our method can achieve high positioning accuracy even when the RX orientation is unknown. The results can further verify the feasibility of the system. In addition to the orientation predictability, the trained DNN can also regulate the algorithm time for each position.

Indoor Visible-Light 3D Positioning System Based on GRU Neural Network

With the continuous development of artificial intelligence technology, visible-light positioning (VLP) based on machine learning and deep learning algorithms has become a research hotspot for indoor positioning technology. To improve the accuracy of robot positioning, we established a three-dimensional (3D) positioning system of visible-light consisting of two LED lights and three photodetectors. In this system, three photodetectors are located on the robot’s head. We considered the impact of line-of-sight (LOS) and non-line-of-sight (NLOS) links on the received signals and used gated recurrent unit (GRU) neural networks to deal with nonlinearity in the system. To address the problem of poor stability during GRU network training, we used a learning rate attenuation strategy to improve the performance of the GRU network. The simulation results showed that the average positioning error of the system was 2.69 cm in a space of 4 m × 4 m × 3 m when only LOS links were considered and 2.66 cm when both LOS and NLOS links were considered with 95% of the positioning errors within 7.88 cm. For two-dimensional (2D) positioning with a fixed positioning height, 80% of the positioning error was within 9.87 cm. This showed that the system had a high anti-interference ability, could achieve centimeter-level positioning accuracy, and met the requirements of robot indoor positioning.

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.

Design of OCC Indoor Positioning System Based on Flat Panel Light and Angle Sensor Assistance

Visible light positioning (VLP) technology is a classic application of visible light communication (VLC), which inherits the advantages of VLC and applies it to the field of positioning. LED (light-emitting diode) is a type of light source. Because of its high brightness, aesthetically pleasing characteristics, and ease of installation, it is used in a variety of indoor lighting applications. However, most of the current VLP technology is still in the laboratory simulation stage and cannot be used in industry or life on a large scale due to various reasons, such as accuracy and cost. Because of the large size of LED flat panel lamps, there are almost no VLP applications with LED flat panel lamps as the emitting light source. Therefore, this paper proposes a VLP technology combining LED flat panel light and a barcode, with a single flat panel light at the transmitting end and a smartphone with a camera at the receiving end, to achieve fuzzy positioning. The paper further uses the angle sensor to assist in designing the “pseudo-two-light positioning” algorithm and selects 16 test points for experiments, and the average positioning error can reach a minimum of 6.5023 cm, achieving centimeter-level positioning accuracy requirements.

LED lighting area recognition for visible light positioning based on convolutional neural network in the industrial internet of things.

In the industrial environment, the positioning of mobile terminals plays an important role in production scheduling. Visible light positioning (VLP) based on a CMOS image sensor has been widely considered as a promising indoor positioning technology. However, the existing VLP technology still faces many challenges, such as modulation and decoding schemes, and strict synchronization requirements. In this paper, a visible light area recognition framework based on convolutional neural network (CNN) is proposed, where the training data is the LED images acquired by the image sensor. The mobile terminal positioning can be realized from the perspective of recognition without modulating LED. The experimental results show that the mean accuracy of the optimal CNN model is as high as 100% for the two-class and the four-class area recognitions, and is more than 95% for the eight-class area recognition. These results are obviously superior to other traditional recognition algorithms. More importantly, the model has high robustness and universality, which can be applied to various types of LED lights.

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.

Adaptive Weighted K-Nearest Neighbor Trilateration Algorithm for Visible Light Positioning

An adaptive weighted K-nearest neighbor (AWKNN) trilateration positioning algorithm fused with the channel state information (CSI) is proposed to optimize the accuracy of the visible light positioning. The core concept behind this algorithm is to combine the WKNN algorithm with ranging based on the CSI. The direct path distance estimated by the CSI is utilized to construct a position set consisting of multiple positions and a corresponding distance database containing multiple distance vectors. The error parameters of the weighted combinations of different distance vectors are calculated iteratively to evaluate the impact of different K-values and weights on the positioning accuracy. The proposed algorithm can achieve high-precision trilateration positioning by adaptively selecting the K-value and weight. A typical 4 m × 4 m × 3 m indoor multipath scene with four LEDs is established to simulate the positioning performance. The simulation results reveal that the mean error of the CSI-based AWKNN algorithm achieves 1.84 cm, with a root mean square error (RMSE) of 2.13 cm. Compared with the CSI-based least squares (LS) method, the CSI-based nonlinear LS method, and the CSI-based WKNN method, the average error of this method is decreased by 29%, 16%, and 17%, whereas the RMSE is reduced by 35%, 14%, and 19%.