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) 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.

As a commonly used indoor Internet of Things (IoT) positioning method, fingerprinting is frequently carried out by fusing inertial and magnetic data. However, magnetic signals may exhibit high similarity in extensive indoor environments, leading to increased mismatching in magnetic fingerprinting positioning results. It negatively impacts the overall accuracy of positioning outcomes, leading to inaccuracies. To address this challenge, this article presents a multilevel error elimination approach that refines magnetic positioning outcomes from coarse to fine-grained adjustments. It marks the inaugural research on error detection and elimination specifically focused on magnetic field positioning. The method employs velocity information, sliding median filtering, and neighborhood filtering to rapidly, accurately, and effectively detect and eliminate the errors of magnetic positioning results. This methodology primarily employs velocity information to rapidly and comprehensively eliminate coarse errors. Subsequently, sliding median filtering addresses scenarios where velocity-based error correction is unreliable, effectively facilitating the intermediate removal of errors in magnetic field positioning outcomes. Ultimately, neighborhood filtering addresses unreliable situations in the above processes, enabling small-scale and detailed elimination of errors in magnetic field positioning results. Within a 100 m$\times $60 m indoor parking lot, the proposed approaches retained 51% to 80% of magnetic positioning results and enhanced the accuracy of magnetic positioning by over 80%.

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.