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.

Indoor localization by smartphones has indicated its promising application prospect in daily life. Smartphone-based pedestrian dead reckoning (PDR) is a common method to obtain the locations. However, PDR suffers from position error accumulation. Although radio frequency (RF) and indoor map can be utilized to restrain the error drift, it requires the prior deployment of facilities or information, which is unsuitable for unknown environments. This paper proposes a factor graph optimization (FGO)-based indoor pedestrian simultaneous localization and mapping (SLAM) with probabilistic exact activity loop closures using a smartphone. In this paper, the smartphone built-in inertial measurement unit (IMU) is solely used to achieve SLAM, in which the human turning activity is regarded as the landmark. Repeatedly observed activities are then used to form loop closures to restrain the drift. FGO is first utilized to formulate pedestrian IMU-only SLAM, which achieves better estimation accuracy than the filter-based method. Moreover, multi-hypothesis tracking is employed to deal with ambiguous data association. During the turning, key points are defined and mutually matched to form exact loop closures to improve estimation accuracy. Simulations and experimental tests are both done to evaluate the performance of the proposed method.

Modelling error of smartphone-reported Global Navigation Satellite System (GNSS) locations plays an important role in urban navigation under mixed LOS/NLOS environments. In the case of pedestrian navigation, the performance of GNSS error modeling significantly affects the precision of final multi-source fusion. In this work, a novel Long Short-Term Memory (LSTM) network is developed for error modeling of smartphone-reported GNSS locations combined with the detected human motion information. The LSTM network is applied to adaptively combine multi-level observations provided by GNSS and built-in sensors-based location sources under a specific time period instead of considering only adjacent timestamps. The motion features extracted from multi-level observations is then modeled as the input vector of LSTM for training and prediction purposes, and the predicted errors under two axis in the n-frame are finally modeled as the error covariance matrix and applied in the multi-sources fusion structure. The comprehensive experiments indicate the effectivity and significant improvement for integrated localization after GNSS error modeling.

Indoor wireless localization toward the next generation Wi-Fi access point has attracted considerable attention due to the presentation of the state-of-art Wi-Fi fine time measurement (FTM) protocol. In order to improve the autonomy, accuracy, and universality of wireless positioning based on the Internet of Things (IoT) terminals, this article proposes a hybrid wireless positioning system which contains the integration of Wi-Fi FTM, crowdsourced received signal strength indicator (RSSI) fingerprinting and micro-electro-mechanical-system (MEMS) sensors (H-WPS). A light-weight pedestrian aimed inertial navigation system (PINS) is proposed, which contains multilevel constraints and a global optimization model in order to eliminate the cumulative error caused by INS update. A deep-learning-based Wi-Fi fingerprinting database generation framework is developed for crowdsourced trajectories evaluation and selection. In addition, three different multisource integration models are applied to fuse the information of PINS, Wi-Fi FTM and RSSI fingerprinting, and calibrate the Wi-Fi ranging bias in real time, which is further enhanced by a novel misclosure check and the multilayer perceptron contained signal quality evaluation strategy. The comprehensive experiments demonstrate that the proposed H-WPS achieves much more precise and universal indoor positioning performance compared with the single location source, and meter-level localization precision can be realized in the Wi-Fi FTM-covered indoor scenes.

Indoor wireless localization using Bluetooth low energy (BLE) beacons has attracted considerable attention due to its extensive distribution and low cost properties. This article proposes a novel 3-D indoor localization algorithm which uses the combination of BLE and multiple sensors (3D-LBMS). The inertial navigation system (INS) and pedestrian dead reckoning (PDR) mechanizations are combined for accurate heading and speed estimation, which contains a multilevel constraints-based quasistatic magnetic field (QSMF) detection algorithm. In addition, dynamic-time-warping (DTW)-based BLE landmark detection algorithm is proposed to provide absolute 3-D location reference to multiple sensors-based positioning method, and the detected BLE landmark points are also used to calibrate the parameter of step-length calculation. Finally, the adaptive unscented Kalman filter (AUKF) is applied to fuse the results of INS/PDR mechanizations, QSMF and locations of detected BLE landmarks to achieve accurate and concrete multisource-based 3-D indoor localization performance. The experimental results show that the proposed 3-D-LBMS is proved to achieve meterlevel 2-D positioning accuracy and submeter level 3-D altitude estimation accuracy in typical indoor environments.

More and more applications of location-based services lead to the development of indoor positioning technology. As a part of the Internet-of-Things ecosystem, most existing indoor positioning algorithms are applied to specific situations, e.g., pedestrian navigation and target detection. To meet the high-precision indoor localization requirement, IEEE 802.11 included the Wi-Fi fine-time measurement (FTM) protocol in 2016, which provides a novel approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point (AP). This article proposes a precise 3-D indoor localization algorithm based on Wi-Fi FTM and smartphone built-in sensors (3D-WFBS). The adaptive extended Kalman filter (AEKF) is used to estimate the pedestrian's real-time heading and walking speed, and the received signal strength indication and round-trip time collected from Wi-Fi APs are combined for proximity detection and providing more accurate ranging results. In addition, the unscented particle filter is applied to fuse the results of AEKF, proximity detection, and Wi-Fi ranging. The experimental results show that compared with the existing dead reckoning method and the other fusion methods, the proposed 3D-WFBS algorithm is proved to achieve meter-level indoor positioning accuracy in typical indoor scenes.