A VLP Approach Based on a Single LED Lamp

Visible light positioning (VLP) is an emerging positioning technique that utilizes indoor light sources to broadcast archer locations through visible light communication (VLC). Benefited by the densely deployed light lamps, VLP holds the promise for more accurate positioning accuracy than RF based approaches, and thus enables interesting applications such as retail navigation and shelf-level advertising in supermarkets and shopping malls. However, we observe that in existing VLP systems, receiving devices are heavily burdened in handling light flickering for VLC. In order to prevent human from being disturbed, the VLC transmitter lamps have to transmit pulses in a high rate (over 1kHz). Since the pulse rate far exceeds the cameras' sampling capability (30fps), receiver design either requires customized light sensor with cumbersome calibration on receiving signal strength [2] or processing high resolution images to leverage rolling shutter effect of the CMOS camera to decode message [1]. The heavy burden on receiving devices motivated us to try getting rid of light flickers. Since human eyes normally can not perceive changes in polarization, the idea is to modulate polarization instead of intensity for communication. In this demo, we demonstrate the VLC system in our VLP design [3] which realizes this idea. It enables lightweight VLC that is even affordable in wearables (Google Glass), without incurring hardware modification or computation off-loading. Moreover, it also makes other types of illuminating light beyond LED light (even sun light) possible to construct communication, therefore eliminates the barrier for deploying future VLC-based applications. As detailed in the full paper [3], our design was inspired by the liquid crystal display (LCD), from which we borrowed polarizer and liquid crystal. Liquid crystal has the property to change the polarization of bypassing polarized light according to the applied voltage. We use polarizer to polarize illuminating light and leverage liquid crystal to modulate bits through changing the light's polarization. The receiver can thus detect changes in polarization with a polarizing film, through which different polarizations result in different intensities. Since the SNR of such communication channel may significantly vary with the mobility of receiving device, we propose to add a dispersion filter to the transmitter. The dispersion filter can cast different frequency of lights into different polarization directions so that SNR in different receiving directions are evened. With the above techniques, VLC transmitter generates signal in low baud rate and mobile devices can perform VLC through camera directly. The communication is long in distance and lightweight enough for resource-constrained devices since the information is carried in intensity changes rather than certain image patterns in rolling shutter approaches. In this demo, we will illustrate the prototype implementation of the VLC communication system. We use incandescent lamps as illuminating light sources. The transmitter consists of a polarizing film, a liquid crystal layer, a dispersor and a control board. The receiver is demonstrated in Android smartphone with polarizing film in front of the camera.

This paper tackles indoor receiver localization as a regression task using visible light positioning (VLP). This technology has gained prominence as an alternative to conventional indoor positioning. Traditional systems such as WiFi and ZigBee face notable drawbacks: electromagnetic interference, added infrastructure, limited accuracy, and high deployment costs. In general, classical positioning approaches—lateration, triangulation, proximity, and least squares—typically infer location from measurements like received signal strength (RSS), time of arrival (TOA), time difference of arrival (TDOA), or angle of arrival (AOA). However, in real-world VLP scenarios, these methods face difficulties since they depend on ideal signals, which means unaffected by physical obstacles and perfectly aligned between transmitters and receiver. Our work proposes the application of the extreme learning machine (ELM) algorithm, its regularized version (R-ELM) and the Levenberg-Marquardt algorithm (LMA), to predict the receiver’s position based simply on the light signals emitted by light-emitting diodes (LEDs) and captured by a photodiode (PD). To evaluate the overall performance of these models, two experimental datasets were used: one in a 2D environment with 7344 RSS samples and another novel in a 3D environment with 27000 training records and 18000 testing samples by analyzing power spectral densities. To assess performance and analyze parameter optimization of ELM, R-ELM, and LMA, metrics such as root mean square error (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RMSE</i>), correlation coefficient (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i>), training and testing times were used. By considering the state of the art, the LMA model proved to be the most accurate in the 2D environment (RMSE less than 10 cm), although its training time was excessive (over one hour), incompatible with for real-time applications (time variant channels). In contrast, the R-ELM achieved an accuracy of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RMSE</i> = 12.4806 cm and completed its training in 31.2 ms (real-time learning), positioning it as a much more efficient alternative compared to LMA. For the 3D dataset, the R-ELM also showed the best overall performance, with an average RMSE close to 22.4784 cm and a training time of 1.0250 s. Incorporating both the dominant frequency and power as inputs into the ELM enhanced the model’s accuracy compared to using each input separately. Lastly, RMSE was evaluated as a function of the distance from the center of the room of each data set, concluding that the R-ELM is more stable in areas with weak signals or missing data, which is particularly valuable in real environments where the signal is not always homogeneous. Finally, the results demonstrate that it is possible to achieve a precise and fast positioning system based on VLP using the R-ELM, as this model generally achieves higher accuracy and computational efficiency.

Visible light positioning (VLP) has been studied widely due to its high accuracy and low cost in the field of location-based services (LBS). However, many existing VLP systems have the requirements that the receiver should be placed horizontally and more than three LED lamps should be used, which are difficult to meet in practical scenarios. Therefore, it is necessary to develop a novel VLP algorithm for tilted conditions. An effective and simple VLP system while the receiver is tilted based on double-LED lamps is proposed in this paper. The vertical position can be determined by combining the information from angle sensors with geometric information. Through analyzing the imaging characteristics of the tilted state, we can utilize the relationship of similarity to calculate the location of the mobile receiver. Experimental results show that the positioning accuracy of our proposed algorithm can reach 5.48 cm.

In this paper, we propose a novel visible light positioning (VLP) scheme under multi-path propagation, which is a practical scenario that has not been well studied for VLP. The new scheme exploits the so-called dual-mode light-emitting diode (DM-LED) of different radiation lobe mode numbers at the transmitter and a photodiode at the receiver. Specifically, we devise a method that utilizes a radiation angle measurement approach for DM-LED, derive the Cramér-Rao lower bound (CRLB) of the estimated distance and analyze the characteristics of key parameters. In addition, two localization algorithms based on linear and nonlinear least squares methods are developed. Simulation results show that under the ideal light-of-sight scenario, the CRLB of the proposed VLP scheme can be close to that of the conventional received signal strength (RSS) aided VLP scheme. Furthermore, we implement the first ever prototype DM-LED lamp to validate the new DM-LED aided VLP system model. Both simulation and experiment results demonstrate that the proposed system outperforms its RSS-aided VLP counterpart in terms of positioning accuracy in the more realistic, and thus more challenging multi-path scenario, even with a tilting receiver.

Train position information is the key factor to ensure the safe and efficient operation of trains for CBTC (Communications Based Train Control) systems. Aiming at the unique characteristics of tunnel environment and train operation for urban rail transit, a train positioning method using VLC(visible light communication)and RSSI (Received Signal Strength Indication) has been proposed, and the visible light communication train positioning system is established. Firstly, based on the classical illumination model of Lambert, a train positioning model with LED lamps for CBTC systems has been established. Secondly, The visible light communication positioning system is designed. Thirdly, the linear distance between the LED lamps and the train on the tunnel wall is calculated by using the RSSI value, and the train positioning results are fitted by using the trilateral positioning principle and the least square method. Finally, according to the real line data and equipment parameters from Chengdu Metro Line 1, the feasibility and effectiveness of the proposed method has been verified. The simulation results show that the maximum positioning error is 9. 4cm, and the average positioning error is less than 10cm,all of these meet the CBTC train positioning requirements. VLC-based positioning can be used as a new choice of train positioning method.

An interacting multiple model based on unscented Kalman filter (IMM-UKF) is widely applied to positioning and tracking targets in various tracking scenarios. At the same time, visible light positioning (VLP) is developing rapidly due to the low cost and accuracy. Therefore, indoor positioning and tracking based on VLP combined with the IMM-UKF has attracted considerable interest. However, existing algorithms work on the assumption that the light-emitting diodes used for tracking are all point light sources, which ignores the geometry of these transmitters and results in low tracking accuracy. To overcome this problem, this paper proposes an innovative tracking algorithm based on VLP. This algorithm considers the shapes of the lights in combination with existing tracking algorithms, such as IMM-UKF. Simulation results show that in a standard Gaussian noise environment, the larger the transmitter is, the more meaningful the proposed algorithm is. INDEX TERMS Target positioning and tracking, visible light positioning (VLP), interacting multiple model (IMM), unscented Kalman filter (UKF), shapes of transmitters.

The accuracy of the visible light positioning (VLP) system is improved by using regression-based and interpolation-based approaches. These approaches are mixed with the received signal strength in both linear and nonlinear least squares techniques. The simplicity of the model is the key point, where a highly accurate VLP system with only one equation “interpolation” is used instead of many complex equations. Gaussian noise and reflections up to three bounces with high reflectivity are the source of noise. In addition, the strength of the methods is studied by changing some affecting parameters. In a previous work, we introduced the regression-based approach in VLP-based systems with one reflection. Here, we extend the work with three reflections, leading to an increase in noise to examine system robustness. The approaches are compared to the traditional ones, and show a remarkable enhancement, e.g., the average error has been improved by 67%.

Visible light positioning (VLP) has developed rapidly with the ubiquitous lighting infrastructure in recent years. Based on classic trilateration or triangulation, VLP usually requires at least three light sources which makes it infeasible in some scenarios where sparse LED luminaries are mounted. In this paper, we focus on VLP in such scenarios and propose a VLP method based on a single LED luminaire as the light transmitter and commercial off-the-shelf smartphone as the receiver. A critical problem that needs to be addressed is to derive the accurate orientation of the smartphone. Although the inertial measurement unit of the smartphone can be used to obtain the orientation, the measurement error severely degrades the positioning accuracy. To address the measurement error, this paper innovates in exploiting projective geometry to calibrate the orientation measurements. We build an experimental platform to evaluate the proposed VLP method. The experimental results show that the proposed VLP method can achieve a submeter accuracy and the indoor positioning errors are within 0.16 m by 90%.

Visible Light Positioning (VLP) is widely recognized as a cost-effective solution for indoor positioning with increasing demand. However, the nonlinearity and highly complex relationship between three-dimensional world coordinate and two-dimensional image coordinate hinders the good performance of image-sensor-based VLP. Therefore, there is a need to develop effective VLP algorithms to locate the positioning terminal using image sensor. Besides, due to the high computational cost of image processing, most existing VLP systems do not achieve satisfactory performance in terms of real-time ability and positioning accuracy, both of which are significant for the performance of indoor positioning system. In addition, the accurate identification of the ID information of each LED (LED-ID) is important for positioning, because if the LED-ID is not recognized well, the positioning can only be achieved in a particular positioning unit and cannot be applied to a large scene with many LEDs. Therefore, an effective image-sensor-based double-light positioning system is proposed in this paper to solve the above problems. We also set up relevant experiments to test the performance of the proposed system, which utilizes the rolling shutter mechanism of the Complementary Metal Oxide Semiconductor (CMOS) image sensor. Machine learning was used to identify the LED-ID for better results. Simulation results show that the proposed double-light positioning system could deliver satisfactory performance in terms of both the real-time ability and the accuracy of positioning. Moreover, the proposed double-light positioning algorithm has low complexity and takes the symmetry problem of angle into consideration, which has never been considered before. Experiments confirmed that the proposed double-light positioning system can provide an accuracy of 3.85 cm with an average computing time of 56.28 ms, making it a promising candidate for future indoor positioning applications.

Indoor positioning systems based on visible light communication (VLC) using white light-emitting diodes (WLEDs) have been widely studied in the literature. In this paper, we present an indoor visible-light positioning (VLP) system based on red–green–blue (RGB) LEDs and a frequency division multiplexing (FDM) scheme. This system combines the functions of an FDM scheme at the transmitters (RGB LEDs) and a received signal strength (RSS) technique to estimate the receiver position. The contribution of this work is two-fold. First, a new VLP system with RGB LEDs is proposed for a multi-cell network. Here, the RGB LEDs allow the exploitation of the chromatic space to transmit the VLP information. In addition, the VLC receiver leverages the responsivity of a single photodiode for estimating the FDM signals in RGB lighting channels. A second contribution is the derivation of an expression to calculate the optical power received by the photodiode for each incident RGB light. To this end, we consider a VLC channel model that includes both line-of-sight (LOS) and non-line-of-sight (NLOS) components. The fast Fourier transform (FFT) estimates the powers and frequencies of the received FDM signal. The receiver uses these optical signal powers in the RSS-based localization application to calculate the Euclidean distances and the frequencies for the RGB LED position. Subsequently, the receiver’s location is estimated using the Euclidean distances and RGB LED positions via a trilateration algorithm. Finally, Monte Carlo simulations are performed to evaluate the error performance of the proposed VLP system in a multi-cell scenario. The results show a high positioning accuracy performance for different color points. The average positioning error for all chromatic points was less than 2.2 cm. These results suggest that the analyzed VLP system could be used in application scenarios where white light balance or luminaire color planning are also the goals.

Indoor localization of high accuracy has been widely interested. Among competitive solutions, visible light positioning (VLP) is promising due to its ability to deliver high-accuracy 3-D position and orientation with low-cost sensors by sharing the LED lighting infrastructure widespread in buildings. Most VLP systems require a prior LED location map for which manual surveys are costly in practical deployment at scale. In this article, to address this difficulty, we propose a novel system for efficient and accurate offline mapping of LEDs for VLP. With input from visual&#x2013;inertial sensors and existing or surveyed priors, it builds the map by posing a full simultaneous localization and mapping (SLAM) problem within a factor graph formulation. Compared to manual surveys, it greatly saves human labor and time while yielding an accurate and workspace-aligned LED map. With real-world experiments in a room-scale testbed and a <inline-formula> <tex-math notation="LaTeX">$15\times $ </tex-math></inline-formula> larger lab office, we extensively evaluate the LED mapping system to verify its efficacy and performance gains.

We propose and experimentally demonstrate visible-light positioning (VLP) systems using silicon photovoltaic cells (Si-PVCs) and machine learning and neural network algorithms. Both angle-of-arrival (AOA)-based and received-signal-strength (RSS)-based VLP systems are evaluated and compared. The Si-PVC could also provide energy harvesting to store received optical power for the mobile unit. Here, second-order linear regression machine learning (RML) model and two-layer neural network are implemented in both AOA-based and RSS-based VLP systems to enhance the positioning accuracy. The root mean square (RMS) average positioning error of the AOA-based VLP system is reduced from 7.22 to 3.46 and to 2.99 cm when using the RML and neural network, respectively. The RMS average positioning error of the RSS-based VLP system is reduced from 7.07 to 3.01 and to 2.60 cm when using the RML and neural network, respectively. The experimental results clearly illustrate that the proposed schemes can significantly improve the positioning accuracy.

Visible Light Positioning (VLP) has attracted much research effort recently. Most existing VLP approaches require special designed light or receiver, collecting light information or strict user operation (e.g., horizontally holding the mobile phone). This incurs a high deployment, maintenance and usage cost. We present RainbowLight, a low-cost ambient light 3D localization approach that is easy to deploy in today's buildings. Our key finding is that light through a chip of polarizer and birefringence material produces specific interference and light spectrum at different directions to the chip. We derive a model to characterize the relation for direction, light interference, and spectrum. Exploiting the model, RainbowLight calculates the direction to a chip after taking a photo containing the chip. With multiple chips, RainbowLight designs a direction intersection based method to derive the location. We implement RainbowLight and extensively evaluate its performance in various environments. The evaluation results show that RainbowLight achieves an average localization error of 3.3 cm in 2D and 9.6 cm in 3D for light on, and an error of 7.4 cm in 2D and 20.5 cm in 3D for light off scenario in the daytime.

Visible light positioning (VLP) has drawn great attention in the field of indoor positioning as light communication has been popularized in low-cost Internet-of-Things (IOT) devices. In this paper, we investigate the VLP problem using the received signal strength (RSS) and by only considering the line-of-slight (LOS) propagation. The RSS-based VLP problem is highly nonlinear, and its solutions may be trapped in local optima without a good initial guess. To circumvent this difficulty, we propose closed-form solutions of the VLP problem considering a known or unknown user orientation. By applying the weighted least squares (WLS) method, the closed-form solutions are divided into two stages. In the stage-one WLS solution, the nonlinear VLP problem is transformed into a pseudo-linear form by introducing some auxiliary variables, which are considered to be independent of each other. The estimates of the stage-one WLS solution are further refined in the stage-two WLS solution by exploiting the constrained relationships among these defined variables. The simulation results show that the stage-two WLS solution provides good estimates for the user position and orientation. The proposed stage-two WLS solution outperforms the existing methods especially at a high signal-to-noise ratio (SNR).

This article reports a novel visible light positioning (VLP) system and the associated experimental results. The developed VLP system is completely passive as it does not require a tracked object to carry any active device or tag. At the same time, it does not require any modification to the existing lighting infrastructure. The positioning system, termed Watchers on the Wall (WoW), localizes a target based on measuring the change it creates in the received signal strength (RSS) of the ambient light recorded at an array of light-sensors embedded in the wall. A prototype system has been implemented and tested to investigate the performance of the proposed approach with regard to localization and tracking. The experimental results show that the median and 90-percentile localization errors of 7 and 21 cm, respectively, can be achieved for a 2 m × 3.6 m testbed. The effect of various parameters like the height of placement and the number of light-sensors, as well as the size of the fingerprint database, has also been studied. The impact of various distance metrics on the localization performance of the weighted K-nearest neighbor (WKNN) classifier has been investigated. It has been found that two distance metrics outperform the commonly employed Euclidean metric. The experimental results also demonstrated that the developed system could track a mobile target along multiple routes with a median error of 12 cm.