Light Positioning System Research Articles

Data-Efficient Training of Gaussian Process Regression Models for Indoor Visible Light Positioning.

A data-efficient training method, namely Q-AL-GPR, is proposed for visible light positioning (VLP) systems with Gaussian process regression (GPR). The proposed method employs the methodology of active learning (AL) to progressively update the effective training dataset with data of low similarity to the existing one. A detailed explanation of the principle of the proposed methods is given. The experimental study is carried out in a three-dimensional GPR-VLP system. The results show the superiority of the proposed method over both the conventional training method based on random draw and a previously proposed line-based AL training method. The impacts of the parameter of active learning on the performance of the GPR-VLP are also presented via experimental investigation, which shows that (1) the proposed training method outperforms the conventional one regardless of the number of final effective training data (E), especially for a small/moderate effective training dataset, (2) a moderate step size (k) should be chosen for updating the effective training dataset to balance the positioning accuracy and computational complexity, and (3) due to the interplay of the reliability of the initialized GPR model and the flexibility in reshaping such a model via active learning, the number of initial effective training data (m) should be optimized. In terms of data efficiency in training, the required number of training data can be reduced by ~27.8% by Q-AL-GPR for a mean positioning accuracy of 3 cm when compared with GPR. The CDF analysis shows that with the proposed training method, the 97th percentile positioning error of GPR-VLP with 300 training data is reduced from 11.8 cm to 7.5 cm, which corresponds to a ~36.4% improvement in positioning accuracy.

Performance Improvement by FRFT-OFDM for Visible Light Communication and Positioning Systems

In indoor visible light communication (VLC) and visible light positioning (VLP) systems, the performance of conventional orthogonal frequency-division multiplexing (OFDM) schemes is often compromised due to the nonlinear characteristics and limited modulation bandwidth of light-emitting diodes, the multipath effect in enclosed indoor environments, and the relative positions of transmitters and receivers. This paper proposes an OFDM scheme based on the fractional Fourier transform (FRFT) to address these issues, demonstrating promising results when applied to indoor VLC and VLP systems. The FRFT, a generalization of the conventional Fourier transform (FT) in the fractional domain, captures information in both the time and frequency domains, offering greater flexibility than the FT. In this paper, we first introduce the computation method of the reality-preserving FRFT for an intensity modulation/direct detection VLC system and integrate it with OFDM to optimize system performance. By adopting FRFT-OFDM under the optimal fractional order, we enhance both the bit error ratio (BER) performance and positioning accuracy. Simulation results reveal that the FRFT-OFDM scheme with an optimized fractional order significantly improves the BER and positioning accuracy compared to the FT-OFDM scheme across most receiver positions within the indoor observation plane. For communication, the FRFT-OFDM scheme achieves over 6 dB Eb/N0 gain compared to the FT-OFDM scheme at a BER of 3×10−4 when the receiver is positioned at most locations in the room. For positioning, the FRFT-OFDM scheme enhances positioning accuracy by more than 1 cm relative to the FT-OFDM scheme at most locations in the room. Notably, both systems maintain the same computational complexity and spectral efficiency.

SIC-Free Based Indoor Two-User NOMA-VLCP System

In this letter, an integrated dual-user visible light communication and positioning (VLCP) system based on non-orthogonal multiple access (NOMA) is proposed. The system consists of a single light-emitting diode (LED) and five photodiodes (PD), and the adaptive feedback threshold (AFT) algorithm is used to reduce error propagation (EP) to improve noise immunity. The particle swarm optimization (PSO) algorithm is employed to construct a joint optimization function that optimizes the power allocation factor of the two users and the roll-off coefficient of the square-root-raised-cosine(SRRC) filter. The simulation results demonstrate that, in the given indoor environment, the bit error ratio (BER) of each user in the proposed system is lower than the front error correction (FEC) limit and the average positioning errors of the two users are 4.62 cm and 5.74 cm respectively.

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

Asynchronous visible light communication and positioning system based on a multi-carrier orthogonal coding mechanism.

This paper presents an asynchronous visible light communication and positioning (VLCP) system leveraging the multi-carrier orthogonal coding mechanism (MC-OCM) based on complete complementary (CC) codes. Utilizing the superior correlation properties of CC codes, our system first achieves interference-free transmission from multiple access interference and inter-symbol interference, thus simultaneously enhancing the accuracy of data transmission and receiver location in asynchronous code division multiplexing-based VLCP systems. Numerical results confirm the proposed VLCP system's superiority, featuring an average BER below the FEC standard and an average PE less than 3 cm in challenging system settings with asynchronous transmission and multipath propagation.

IRS aided visible light positioning with a single LED transmitter

We propose a visible light positioning (VLP) system with a single light emitting diode (LED) transmitter and an intelligent reflecting surface (IRS) for estimating the position of a receiver equipped with a single photo-detector. By performing a number of transmissions from the LED transmitter and optimizing the orientation vectors of the IRS elements for each transmission, position information is extracted by the receiver based on power measurements of the signals reflecting from the IRS. The theoretical limit and the maximum likelihood (ML) estimator are presented for the proposed setting. In addition, an algorithm, named IRS focusing, is proposed for determining the orientations of the IRS elements during the localization process. The effectiveness of the proposed localization approach is demonstrated through simulations. Furthermore, extensions are provided to apply the proposed approach in the presence of partial prior information about the receiver position and when the IRS is located at the LED transmitter.

Frequency division multiplexing-based light-emitting diode identification expansion design in visible light imaging positioning system

Frequency division multiplexing-based light-emitting diode identification expansion design in visible light imaging positioning system

Acpro: fusion of embedded ambient light sensor and image sensor for visible light positioning

Abstract With the increasing demand for indoor positioning, visible light positioning (VLP) systems have become an optics technologies research hotspot due to their high accuracy and avoidance of electromagnetic interference. However, current photodiode (PD)-based and image sensor (IS)-based VLP systems still suffer from high costs at the receiver, and the problem of the trade-off between field of view (FoV) and positioning accuracy. Therefore, we propose a smartphone-based low-cost, and high-precision VLP system, Acpro. The system combines the smartphone’s embedded ambient light sensor and IS as the receiver for the positioning. First, for the low sampling rate of the ambient light sensor, we use the frequency aliasing characteristic to identify the LED’s beacon through the dynamic undersampling principle. Then, to increase the FoV and positioning accuracy, we use the standard distance information corrected by the IS to improve the noise distance calculated by the ambient light sensor. Further, the corrected distance information is used for positioning. Extensive results show that our system can achieve sub-meter positioning accuracy.

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.

ULiDR: An inertial-assisted unmodulated visible light positioning system for smartphone-based pedestrian navigation

Visible Light Positioning (VLP) techniques have attracted considerable attention owing to their cost-effectiveness, high precision, and ubiquitous infrastructure. However, conventional VLP demands supplementary hardware for Light Emitting Diode (LED) modulation, which entails retrofitting existing indoor light systems for positioning applications. Thus, it is costly for wide-scale adoption. Moreover, the built-in Photodiodes (PDs) in smartphones can only provide a limited sampling rate (around 10 Hz), making it challenging to identify LEDs and extract each LED’s light intensity by Frequency Division Multiplexing and Fast Fourier Transform. To alleviate these problems, we develop a novel inertial-aided Unmodulated Visible Light Positioning system (uLiDR) for resource-constrained platforms like smartphones. Firstly, we evaluate the feasibility of unmodulated LED for precise ranging using smartphones. Next, we introduce a selective fusion strategy to mitigate LED occlusion in pedestrian navigation. As slight tilts of the phone are unavoidable during walking, we leverage attitude information calculated by the built-in sensors to handle the phone’s tilt in VLP. Then, we implement the tightly-coupled integration of uVLP and Pedestrian Dead Reckoning (PDR) in an optimization-based framework. The initial location is vital for PDR, and thereby, we proposed an Angle of Arrival (AoA)-based method to provide a precise initial location estimation for PDR. Experimental results show that the proposed uLiDR can achieve precise pedestrian navigation and effectively suppress the error accumulation of PDR.

NLOS visible light positioning and communication based on LoRa modulation.

With the rapid development of the Internet of Things, location-based services are becoming increasingly important, especially in indoor environments. Visible light positioning (VLP) has garnered widespread attention due to its high accuracy, low cost, and immunity to the radio frequency electromagnetic interference. However, traditional VLP relies on line-of-sight paths, making it impractical in complex and dynamic indoor environments. In this paper, we propose a non-line-of-sight (NLOS) visible light positioning and communication system based on LoRa modulation to address the issue of link obstruction. LoRa is employed to recover position information transmitted from light emitting diodes (LEDs) over NLOS links, enabling reliable communication under weak lighting conditions. We establish a geometric relationship between the LED and the virtual image of the photodetector (PD). Leveraging the NLOS channel model, we derive the relationship between the received signal strength and the distance from LEDs to the virtual image of the PD. Through this relationship, the trilateration method is applied to calculate the position of the receiver. Based on experimental results, the proposed system achieves 90th percentile localization accuracy of less than 25 and 35 cm for the PD heights of 60 and 80 cm, respectively.

Integrated Mobile Visible Light Communication and Positioning Systems Based on Decision Feedback Channel Estimation

Visible light communication (VLC) and visible light positioning (VLP) systems are usually designed separately to prevent mutual interference, while terminal mobility often introduces challenges that can degrade their performance. In this paper, we propose an integrated visible light communication and positioning (VLCP) scheme designed for mobile scenarios, encompassing both multiple-input–single-output (MISO) and multiple-input–multiple-output (MIMO) configurations. This scheme integrates both functionalities into a unified system. Utilizing decision feedback channel estimation (DFCE), we effectively estimate the dynamic channel state information (CSI) for both VLC and VLP, thereby potentially enhancing both communication and positioning performance. Simulation results across various routes verify the effectiveness of the proposed scheme. It is observed that when the terminal moves at 1 m/s, the VLCP scheme with DFCE can maintain reliable transmission quality, ensuring bit error rates (BERs) consistently below 1.3 × 10−2. Additionally, the mean positioning errors remain within the centimeter range in different routes, not exceeding 4.3 cm and 15.5 cm in the MISO and MIMO scenarios, respectively.

Single-Source VLCP System Based on Solar Cell Array Receiver and Right-Angled Tetrahedron Trilateration VLP (RATT-VLP) Algorithm

A significant deployment limitation for visible light communication and positioning (VLCP) systems in energy- and light-source-restricted scenarios is the reliance of photodetectors (PDs) on external power supplies, compromising sustainability and complicating receiver charging. Solar cells (SCs), capable of harvesting and converting environmental light into electrical energy, offer a promising alternative. Consequently, we first propose an indoor VLCP system that utilizes an SC array as the receiver, alongside a right-angled tetrahedron trilateration visible light positioning (RATT-VLP) algorithm based on a single light source and multiple receivers. The proposed system uses an SC array in place of PDs, utilizing binary phase shift keying (BPSK) signals for simultaneous communication and positioning. In experiments, we verified the system’s error-free communication rate of 1.21 kbps and average positioning error of 3.40 cm in a 30 cm × 30 cm area, indicating that the system can simultaneously satisfy low-speed communication and accurate positioning applications. This provides a viable foundation for further research on SC-based VLCP systems, facilitating potential applications in environments like underwater wireless communication, positioning, and storage tank inspection.

Optimisation of the Transmitter Layout in a VLP System Using an Aperture-Based Receiver

In this paper, we consider a visible light positioning (VLP) system, where an array of photo diodes combined with apertures is used as a directional receiver and a set of inexpensive and energy-efficient light-emitting diodes (LEDs) is used as transmitters. The paper focuses on the optimisation of the layout of the transmitter, i.e., the number and placement of the LEDs, to meet the wanted position estimation accuracy levels. To this end, we evaluate the Cramer–Rao bound (CRB), which is a lower bound on the mean-squared error (MSE) of the position estimate, to analyse the influence of the LEDs’ placement. In contrast to other works, where only the location of the LEDs was considered and/or the optimisation was carried out through simulations, in this work, the optimisation is carried out analytically and considers all the parameters involved in the VLP system as well as the illumination. Based on our results, we formulate simple rules of thumb with which we can determine the spacing between LEDs and the minimum number of LEDs, as well as their position on the ceiling, while also taking into account the requirements for the illumination.

Neural Network-Based Detection of OCC Signals in Lighting-Constrained Environments: A Museum Use Case

This research presents a novel approach by applying convolutional neural networks (CNNs) to enhance optical camera communication (OCC) signal detection under challenging indoor lighting conditions. The study utilizes a smartphone app to capture images of an LED lamp that emits 25 unique optical codes at distances of up to four meters. The developed CNN model demonstrates superior accuracy and outperforms traditional methodologies, which often struggle under variable illumination. This advancement provides a robust solution for reliable OCC detection where previous methods underperform, particularly in the tourism industry, where it can be used to create a virtual museum on the Unity platform. This innovation showcases the potential of integrating the application with a virtual environment to enhance tourist experiences. It also establishes a comprehensive visible light positioning (VLP) system, marking a significant advance in using CNN for OCC technology in various lighting conditions. The findings underscore the effectiveness of CNNs in overcoming ambient lighting challenges, paving the way for new applications in museums and similar environments and laying the foundation for future OCC system improvements.

Visible light positioning system using a smartphone's built-in ambient light sensor and inertial measurement unit.

In recent years, the visible light positioning field has experienced remarkable advancements. However, smartphones find it difficult to identify light-emitting diode (LED) and extract each LED's light signal intensity due to the low-frequency and uneven sampling of built-in ambient light sensors (ALS, which is a photodiode that measures ambient light in lux units). Thus, traditional visible light positioning systems cannot be directly applied to smartphones. In this Letter, we propose a single-light visible light positioning system using a non-modulated LED as an emitter, the built-in ALS as the receiver, and the inertial measurement unit of the smartphone to assist in measuring the smartphone's attitude. It only requires the user to turn the smartphone by a few angles in a stationary position to estimate its current three-dimensional (3D) spatial position. This method does not require modification of the existing lighting system and consumes less power than the camera-based visible light positioning (VLP) systems. We have built an experimental site measuring 5 m × 5 m × 2.2 m to evaluate the performance of the positioning system, and the preliminary results show that the proposed system achieves sub-meter-level positioning accuracy.

A study on resource allocation scheme for self-powered visible light communication and positioning systems

In this work, a novel resource allocation scheme is proposed for the self-powered visible light communication and positioning (SPVLCP) system based on orthogonal frequency division multiplexing (OFDM). This scheme can balance the performance of communication, positioning, and energy harvesting by optimizing the power splitting (PS) at the receiver and power allocating on each subcarrier using the multi-verse optimization (MVO) algorithm. The performance of the SPVLCP system using the proposed resource allocation scheme is investigated through simulation and experiment for different constraint conditions and transmission rates. The results indicate that the SPVLCP system using the proposed resource allocation scheme has better performance compared with that using the uniform power allocation scheme.

K-Means-Based DNN Algorithm for a High Accuracy VLP System

In this paper, a positioning algorithm based on the combination of K-means clustering and deep neural networks (DNNs) is first presented for multiple light emitting diodes (LEDs) integrated with visible light positioning (VLP) systems. We extracted the maximum value from the collected optical power of LEDs, utilizing the ratio of each optical power to this maximum optical power as the input training data. The experimental results demonstrate that the proposed algorithm outperformed the conventional DNN algorithm in terms of anti-jamming capability and positioning accuracy. In addition, the positioning accuracy of the proposed system reached a millimeter level, which is the highest experimental VLP accuracy, to the best of our knowledge.

Experimental demonstration of an indoor visible light positioning system using RGB LEDs for multi‐cell networks

AbstractA visible light positioning (VLP) system for multi‐cell networks based on multi‐colour LED that combines frequency division multiplexing with the received signal strength and a trilateration method is proposed. First, it employs experimental measurements to model the designed visible light communication (VLC) system under the characteristics of the target scenario. Second, it introduces a low‐cost VLC transmitter that exploits the chromatic space to transmit the VLP information. Third, it characterizes the VLC transmitter and proposes a linearization for electro‐optical responses of RGB LED. An RGB digital colour sensor and simple method calibrate the chromatic emission. The experimental results show that the proposed VLP multi‐cell architecture achieves a positioning accuracy lower than .

Centimeter-Level 3-D Mobile Online Visible Light Positioning System With Single LED Lamp

In this paper, we consider a practical indoor 3D mobile online visible light positioning (VLP) system, where the orientation of the UE is arbitrary. Based on the received signal strength (RSS) of multiple photo-detectors (PDs), we formulate the 3D VLP problem as a non-linear least squares (NLS) optimization problem, and then propose a sequential quadratic programming (SQP) positioning algorithm to efficiently calculate UE’s location. To obtain more accurate positioning solutions, we further leverage the advantages of deep learning and develop a stochastic gradient descent (SGD) based VLP algorithm, and achieve an average positioning error of 1.77cm, which significantly outperforms existing RSS VLP localization methods. Moreover, we design a 3D mobile online VLP system prototype by using a portable RaspberryPi 4 Model B as the positioning signal processor and data memory, and establish the first publicly available 3D VLP measured dataset including both RSS and orientation. The proposed positioning schemes are implemented and evaluated via the designed prototype system, which can achieve centimeter-level positioning accuracy (below 1 cm in certain condition).