Visible Light Positioning (VLP) has emerged as a promising indoor localization technology owing to its high accuracy, low power consumption, and lighting compatibility. The Received Signal Strength (RSS)-based multi-anchor VLP pre-serves these advantages while having drawn considerable research attention due to its simple implementation and high reliability. However, its large-scale deployment encounters challenges at every stage: inefficient anchor calibration, limited model-based ranging performance, and robustness reduction from undetected gross errors. To address these issues, we propose a VLP and inertial navigation system fusion framework comprising an anchor position estimation module, a distance estimation module, and a fusion positioning module. For anchor calibration, a LiDAR-inertial odometry-based calibration scheme enhanced by a twolayer optimization strategy is introduced, which provides prior knowledge of anchor positions for the whole system. To improve the performance of the RSS-based ranging method, a hybrid Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-Bidirectional Long Short-Term Memory (Bi-LSTM) network with an embedded distance quality assessor is developed, achieving over 50% higher accuracy than model-based baselines. It delivers precise distance estimates and their validity labels for measurement updates in subsequent fusion positioning. Additionally, a two-stage error detection mechanism filters low quality observations by combining network-generated usability labels with prior-state estimates. The system consistently attains decimeter-level positioning across various trajectories, meeting the needs of diverse Internet of Things applications.

Visible light positioning (VLP) is one of the most promising technologies for providing high-precision, low-cost indoor positioning and navigation services. However, the traditional calibration methods of VLP are complex and unfriendly to users, which hinders the large-scale commercial deployment of VLP systems. In this paper, a fast and robust calibration method for received signal strength (RSS)-based VLP system is proposed, which dispenses with geometric measurement and greatly simplifies the calibration procedures. The proposed method calibrates the Lambert model through two steps, firstly using a ratio method to calibrate the Lambert order, and then estimating the constant term. The actual processes only require moving a robot equipped with a photo-detector (PD) along a rectangular trajectory once, and then the program will automatically estimate the required parameters by analyzing the RSS during this period. Experimental results show a good stability of the calibrated parameters, as well as a excellent distance measuring accuracy within 12 cm. The proposed method is 3.7 times more efficient than traditional methods while the positioning accuracy is close, which will greatly reduce the time and labor costs of large-scale deployment.

Visible Light Positioning (VLP) has emerged as a promising indoor localization technology due to its high accuracy, low cost. However, it still faces challenges such as environmental interference, signal noise, and occlusion. To address the above issues, a Bidirectional Encoder Representation from Transformer (BERT)-enhanced VLP and inertial navigation fusion positioning system is developed. Firstly, to tackle the problem of inaccurate ranging caused by signal noise, we propose a Transformer-based network, VLP-BERT, which leverages long-sequence masking to enhance the network’s feature extraction capabilities from visible light signals. Moreover, the VLP-BERT is integrated into an autoencoder-decoder architecture for signal denoising. Secondly, to overcome the limitations of traditional ranging models in complex environments, a deep learning-based centralized VLP ranging model is proposed. Finally, to enhance the system’s reliability under varying conditions, a tightly coupled fusion method integrating VLP with Pedestrian Dead Reckoning (PDR) is proposed, incorporating error detection and state-constrained strategies. Extensive experimental evaluations demonstrate the effectiveness of VLP-BERT in both denoising and accurate ranging. The system was compared with nine different methods, the results show that the proposed tightly coupled approach not only achieves sub-meter-level accuracy but also significantly enhances the system’s robustness, even in challenging scenarios such as signal blockage and poor signal quality.

Visible Light Positioning (VLP) has attracted significant attention due to its low cost, low power consumption and high accuracy. However, challenges such as noise interference and limited coverage still affect the system’s performance. On the other hand, Inertial Measurement Units (IMUs) can provide position measurements that are unaffected by environmental factors, but traditional methods tend to diverge over time. To solve these issues, a method for robust fusion positioning of a deep learning-enhanced pedestrian trolley odometer and VLP has been proposed. Firstly, a deep learning-based inertial odometry (DIO) is introduced to capture gyroscope noise and hidden motion features, which can achieve precise displacement estimation within a specified window size. Secondly, a tightly coupled fusion filter that integrates IMU-based odometry with received signal strength (RSS)-based VLP is designed, it significantly enhances the system’s robustness under conditions of signal sparsity and serious signal noise interference. Finally, a method for calculating the observation error covariance based on RSS that considers measurement uncertainty has been proposed, it ensures that the system remains robust even in the presence of weak signal strength or environmental noise. Experimental evaluations demonstrate that the proposed DIO improves accuracy by 33.4% compared to existing methods, with only an 0.76% increase in average computation time. Furthermore, compared to the DIO, VLP, and particle filter fusion systems, the proposed fusion-based localization system achieves accuracy improvements of 78.1%, 23.4%, and 9.6%, respectively, while maintaining significantly lower average computational time than the particle filter system. These results indicate that the proposed fusion method significantly outperforms existing techniques and effectively addresses the limitations of individual positioning methods.

The escalating demand for indoor location-based services has propelled advancements in indoor positioning technologies in which visible light positioning (VLP) standing out for its accuracy and eco-friendliness. Current VLP systems are typically categorized into multi-anchor-based and single-anchor-based methods. The former encounters challenges like high wiring costs, limited adaptability in narrow spaces, and the segregation of lighting and positioning. Current single-anchor methods using cameras or photodiode (PD) arrays face complex hardware design costs and application limitations. To address these issues, a deep learning-enhanced VLP method with a light-emitting diode (LED) array is proposed. Firstly, a cost-effective single-anchor VLP system is designed, utilizing a LED array and a single PD, which dramatically reduces the costs. Secondly, to mitigate the impact of various noises on the received signal, a transformer-based signal-denoising network is designed, resulting in a significant reduction in the large ranging errors. Furthermore, a distance error estimation network based on graph neural network (GNN) is introduced to address the issue of poor anchor configuration caused by LED arrays. The GNN aggregates inherent correlations among LEDs, significantly reducing ranging errors. Finally, a weighted particle swarm optimization (PSO)-based positioning algorithm is introduced to further optimize results, and GNN’s outputs are converted into weights for PSO using a self-designed weight function, enhancing accuracy and robustness. Experimental evaluations of the hardware and proposed methods demonstrate sub-meter-level positioning accuracy. Moreover, the effective coverage range is comparable to that of a regular LED fixture of the same size, suggesting its broad applications prospects in the Internet of Things (IoT) after it integrates lighting and positioning capabilities.

Visible light positioning (VLP), characterized by high accuracy, low power consumption, cost effectiveness, and eco-friendliness, has garnered substantial attention in Internet of Things (IoT) positioning applications. However, the commonly used received signal strength (RSS)-based multianchor methods exhibit limitations in addressing certain challenging scenarios, such as tilted receivers, nonuniform light intensity, and narrow spaces, while incurring additional expenses for wiring and controllers. To tackle these challenges, we propose a novel VLP system utilizing a custom-designed light-emitting diode (LED) array. Initially, we conduct an in-depth analysis of the array’s size, layout, and specialized LED design, referencing prevalent household lamps to ensure that the devised array fulfills both illumination and positioning functions. Subsequently, a phase-difference-based ranging and localization method is introduced to supplement the array. By assuming equidistance among the specialized LEDs, a base distance is established for global distance calculations. Our proposed method not only eliminates the need for model calibration in RSS-based approaches but also excels in addressing challenging situations that RSS-based methods fail to resolve. Furthermore, we incorporate the Cramer–Rao lower bound and the geometric dilution of precision to assess the theoretical performance and efficacy of the proposed method. Simulation experiments are performed to evaluate the proposed array and method, taking into account various error sources, such as time synchronization errors and receiver noise. Results demonstrate that our proposed method’s average localization error is less than 10 cm under various influences and diverse scenarios, which can fulfill a wide range of IoT applications’ requirements. Moreover, it holds the potential to replace commonly used household lamps.

Visible Light Positioning (VLP), a promising indoor positioning technique, has gained wide popularity worldwide because of its ubiquitous infrastructure, low power consumption, and high positioning precision. However, VLP systems based on photodiodes (PDs) often suffer from varying ambient light with time and space, which seriously degrades their positioning precision and robustness. In this article, we carefully evaluate the influence of the ambient light on the VLP system, which includes the reduction of positioning accuracy by varying ambient light with time and the inaccurate parameter calibration by unevenly distributed ambient light. Then, we figure out that the influence of ambient light on the Received Signals Strength (RSS) values is determined by the ambient light intensity and PD, which is independent of external factors, including distance, frequency, LED, etc. Next, we propose a new positioning framework, RatioVLP, where a ratio model that is more robust to varying ambient light with time is used. However, the ratio model is severely dependent on the Lambert parameters that are vulnerable to ambient light, which reduces the framework's precision when the calibration area is unevenly covered by ambient light. Thus, we design new parameters that are less sensitive to ambient light, calledR parameter, to connect the RSS ratio and its corresponding distance ratio, which can strengthen the ratio model's robustness and effectively reduce the influence of ambient light on the parameter calibration process. Experimental results show that the positioning precision of the proposed method is improved by more than 50 % when compared to the conventional Lambert model in scenes influenced by ambient light.

The SPiForest, a new isolation-based approach to outlier detection, constructs iTrees on the space containing all attributes by probability density-based inverse sampling. Most existing iForest (iF)-based approaches can precisely and quickly detect outliers scattering around one or more normal clusters. However, the performance of these methods seriously decreases when facing outliers whose nature "few and different" disappears in subspace (e.g., anomalies surrounded by normal samples). To solve this problem, SPiForest is proposed, which is different from existing approaches. First, SPiForest uses the principal component analysis (PCA) to find principal components and estimate each component's probability density function (pdf). Second, SPiForest utilizes the inv-pdf, which is inversely proportional to the pdf estimated from the given dataset, to generate support points in the space containing all attributes. Third, the hyperplane decided by these support points is used to isolate the outliers in the space. Next, these steps are repeated to build an iTree. Finally, many iTrees construct a forest for outlier detection. SPiForest provides two benefits: 1) it isolates outliers with fewer hyperplanes, which significantly improves the accuracy and 2) it effectively detects the outliers whose nature "few and different" disappears in subspace. Comparative analyses and experiments show that the SPiForest achieves a significant improvement in terms of area under the curve (AUC) when compared with the state-of-the-art methods. Specifically, our method improves by at most 17.7% on AUC when compared to iF-based algorithms.

The emerging applications of the Internet of Things (IoT), such as driverless cars, have an increasing need for precise vehicle positioning. Inertial navigation systems (INSs) became a possible component of autonomous driving systems due to low computational load, fast response, and high autonomy. However, error accumulation presents a significant challenge. Although nonholonomic constraints (NHCs) and odometry (ODO) have been demonstrated to improve INS, NHC is not always reliable, and ODO is often inaccessible in many applications. To address these issues, we propose a novel untethered pseudo-odometry, SdoNet, a convolutional neural network that estimates vehicle velocity from raw inertial measurement unit (IMU) observations to extend NHC as a 3-D velocity constraint without needing a hardware-wheeled ODO. To eliminate the influence of interference features on the accuracy of SdoNet, we improve the SdoNet by incorporating a residual module, attention mechanism, and soft threshold to guide the network to eliminate interference features. Moreover, a lightweight noise adapter network is proposed to adjust the constraint measurement noise covariance dynamically to apply the velocity constraint properly. The proposed approach is validated on the KITTI data set, demonstrating that SdoNet enhances the network’s learning ability and achieves robust and accurate velocity regression, especially in noisy IMU observations. The mean absolute speed regression error of SdoNet is lower than the two types of long short-term memory networks by 52.52% and 71.86%, respectively. Compared to the process using only NHC, the absolute translation error is reduced by approximately 44.00% after employing the pseudo-ODO velocity constraint and further reduced by around 11.31% after employing the noise adapter.

As a critical topic of Internet of Things applications, smartphone-based indoor navigation has a rapidly growing need in various applications. However, indoor navigation technology is unreliable when facing a challenge in complex indoor environments. This article presents a tightly coupled (TC) integration of pedestrian dead reckoning (PDR) and Bluetooth for indoor pedestrian navigation and enhances it from three approaches. We first establish a Gaussian-based distance model (GDM) that improves the signal path-loss model to incorporate the prior information on the variation of signal volatility with distance. Then, the use of map information and a back-off strategy to optimize the particle transfer strategy further improves the positioning accuracy and rationality of the system. Moreover, we leverage behavioral landmarks, signal landmarks, and distance information to build a graph optimization model to optimize the proposed navigator. We have extensively verified the proposed navigator and compared it with the existing solutions and systems. Experimental results demonstrated that the average errors of the proposed solutions in three scenes were 34.71% of Bluetooth, 14.04% of PDR, 45.13% of the extended Kalman filter, 57.83% of the unscented Kalman filter, and 56.10% of PF, respectively. The results showed that our proposed solution has apparent advantages, especially when addressing the issues of incorrect trajectory updating and divergence of the system in a complex environment.