Simple and highly accurate indoor visible light positioning system: regression- and interpolation-based approaches

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.

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.

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.

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.

In this letter, a quasi-gapless integrated visible light communication and positioning (VLCP) system is experimentally demonstrated for the first time, based on filter bank multicarrier- based subcarrier multiplexing (FBMC-SCM). Compared with orthogonal frequency division multiplexing-based SCM (OFDM- SCM), FBMC-SCM has lower out-of-band interference (OOBI) and therefore requires much smaller guard band (GB) spacing. A low-complexity transmit diversity transmission scheme is adopted here, where all the light emitting diode (LED) lamps transmit the same communication signal.Moreover, phase difference of arrival (PDOA) is applied for positioning. The experimental results show that, in a coverage area of 1.2×1.2 m2 with a height of 2.1 m, the mean position errors using OFDM–SCM and FBMC-SCM for a GB of 0.7 MHz are 10.91 and 6.08 cm, respectively. Moreover, a comparable bit error rate (BER) performance can be achieved for both OFDM–SCM and FBMC-SCM. Due to the negligible GBs when using FBMC-SCM, the effective bandwidth utilization ratio of the integrated VLCP system is improved from 72% to 98% when OFDM-SCM is replaced by FBMC-SCM.

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.

In visible light positioning (VLP) systems, factors like multipath reflection and noise interference degrade positioning accuracy. To address this challenge, this letter innovatively applies a Transformer-Encoder model to a VLP system with a dual-LED and multi-photodiode (PD) architecture. The proposed Transformer-Encoder model captures the spatial distribution information of the PDs through a Positional Encoding module. Its core Multi-Head Attention mechanism, incorporating positional information, enables the model to focus on critical channel features. This significantly enhances the model's robustness against multipath interference and noise while strengthening its capability to characterize channel features. Simulation results demonstrate that within a 4 m × 4 m × 3 m space, the Transformer-Encoder model achieves an average positioning error of 0.75 cm, with 90% of errors below 1.27 cm. Comparative analysis with other positioning models confirms the high precision of the proposed method.

This paper investigates the error bounds for position estimation in downlink visible light communication (VLC) systems. More precisely, we use Cramér-Rao lower bounds (CRLBs) to study the effect of signal-dependent shot noise (SDSN) on the error performance in visible light positioning. We consider synchronous, quasi-synchronous, and asynchronous visible light positioning (VLP) systems. The system performance is assessed based on the position estimation, and uses information from parameters relating to the time of arrival (TOA), time difference of arrival (TDOA), and received signal strength (RSS). The results demonstrate that SDSN has a negative impact on the error bounds in all considered scenarios. Moreover, the level of degradation observed is not uniform among all cases at higher SDSN levels.

We propose an indoor Visible Light Positioning (VLP) system that augments Lambertian emissions from conventional luminaires with a single steerable laser. By knowing the exact angles between the laser and the receiver being positioned as well as received-signal-strength (RSS) from the Lambertian sources to the receiver, we can predict the position of a receiver in three dimensions (3D). Most RSS-based VLP algorithms assume a constant operating plane (height) of the receiver to stabilize trilateration to produce 2D estimates. By using the angles provided by laser steering, we show that 2D and 3D error can be reduced to near zero for a known height and 3D mean square error is less than 12.89 cm for 95% of the reference volume when the height is unknown a priori. Finally, we show that unlike trilateration-based methods, the technique can operate using as few as a single luminaire, making it more versatile than other RSS-based approaches for establishing receiver position.

Angle-of-arrival (AOA) estimation of optical signals is a key technique in visible light positioning (VLP) systems, which can be realized using photodiode (PD) arrays. However, most of existing works do not properly design the angles of the PDs, which hampers the accuracy of VLP systems. In this work, we optimize the PD array to achieve the highest AOA estimation accuracy. Specifically, we achieve the optimal angle combination of the PDs in order to minimize the power of the additive Gaussian noise imposed on the exact incident vector. Closed-form expressions are derived for the optimal angles of the PD array. Our analytical results show that improper placement of PDs can cause significant accuracy loss. Both simulation and experimental results show that the optimized PD array can suppress the angle estimation noise. The theoretical results provide a benchmark for the design of PD arrays, and lay a foundation for further studies on error analysis of VLP.

In this paper, a novel visible light positioning (VLP) system is proposed. The new system includes multiple dual-mode light-emitting diode (DM-LED) lamps with different radiation lobe mode numbers (RLMN) and a user device equipped with a photodiode (PD). Specifically, a method utilizing a radiation angle measurement (RAM) approach for DM-LED is devised, and the characteristics of key system parameters are studied, taking into account multi-path reflections in practical indoor VLP scenarios. Furthermore, two localization algorithms based on linear and nonlinear least squares methods are developed. Simulation results show that the proposed system significantly outperforms its conventional counterpart in terms of positioning accuracy under the challenging multi-path scenario.

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.

ABSTRACTUsing deep learning to improve the accuracy of indoor visible light positioning (VLP) systems has gradually become a widely used research strategy in the field. However, current deep learning‐based indoor visible light localization algorithms have not been able to effectively mine the deep temporal and spatial sequence features in signals, resulting in complex network construction and low localization accuracy. To address this issue, the text proposes a deep learning framework that utilizes an attention mechanism to train a small number of randomly continuously sampled spatial received signals to predict the coordinates of the received signals and encode the spatiotemporal sequence attributes of the received signals as a feature into the data, constructed a highly reliable spatiotemporal sequence attention mechanism for indoor visible light localization method. Combined with Convolutional Neural Networks (CNN), the localization accuracy is further improved. Through simulation experiments, it has been verified that the neural network structure designed in this paper has better positioning accuracy compared to advanced algorithms, and can still achieve centimeter‐level (9.886cm) average positioning error under low signal‐to‐noise ratio (SNR) conditions. It is proved that the method proposed in this paper is reliable in the indoor VLP system.

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.

Visible light communication and positioning (VLCP) systems have been rarely researched to sense and model the obstacles. However, the accumulating database containing the signal amplitudes and receiver positions can be exploited to detect and reconstruct the positions and shapes of the obstacles. In this work, we propose an algorithm to model the obstacles in a VLCP system, where several light-emitting diodes (LEDs) are set at known positions, and the users equipped with photodiodes (PDs) randomly move to receive the LEDs' signals at different positions. Based on the received signals, the blocked links between the users and LEDs can be distinguished, and thus we can reconstruct the obstacle information. Simulation and analytical results show that the algorithm can estimate the positions and shapes of obstacles. Such an algorithm expand the services of the VLCP systems, and can sense the three-dimensional (3D) information of the environment through the light channel coefficients so as to circumvent the expensive devices, such as laser radars. The proposed algorithm can also support navigation and prediction of wireless channel fading.