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 protocol in 2016 which provides a novel approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point. This paper proposes a precise 3D indoor localization algorithm based on Wi-Fi Fine Time Measurement and smartphone built-in sensors (3D-WFBS). The adaptive extended Kalman filter 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 access points are combined for proximity detection and providing more accurate ranging results. In addition, the unscented particle filter is applied to fuse the results of adaptive extended Kalman filter, proximity detection and Wi-Fi ranging. 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.

More and more applications of location-based services lead to the development of indoor positioning technology. Wi-Fi-based indoor localization has been attractive due to its extensive distribution and low cost properties. IEEE 802.11-2016 now includes a Wi-Fi Fine Time Measurement (FTM) protocol which provides a more robust approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point (AP). To improve the positioning accuracy, in this paper, we propose a robust dead reckoning algorithm combining the results of Wi-Fi FTM and multiple sensors (DRWMs). A real-time Wi-Fi ranging model is built which can effectively reduce the Wi-Fi ranging errors, and then a multisensor multi-pattern-based dead reckoning is presented. In addition, the Unscented Kalman filter (UKF) is applied to fuse the results of Wi-Fi ranging model and multiple sensors. The experiment results show that the proposed DRWMs algorithm can achieve accurate localization performance in line-of-sight/non-line-of-sight (LOS)/(NLOS) mixed indoor environment. Compared with the traditional Wi-Fi positioning method and the traditional dead reckoning method, the proposed algorithm is more stable and has better real-time performance for indoor positioning.

High-precision indoor positioning technology is regarded as one of the core components of artificial intelligence (AI) and Internet of Things (IoT) applications. Over the past decades, society has observed a burgeoning demand for indoor location-based services (iLBSs). Concurrently, ongoing technological innovations have been instrumental in establishing more accurate, particularly meter-level indoor positioning systems. In scenarios where the penetration of satellite signals indoors proves problematic, research efforts focused on high-precision intelligent indoor positioning technology have seen a substantial increase. Consequently, a stable assortment of location sources and their respective positioning methods have emerged, characterizing modern technological resilience. This academic composition serves to illuminate the current status of meter-level indoor positioning technologies. An in-depth overview is provided in this paper, segmenting these technologies into distinct types based on specific positioning principles such as geometric relationships, fingerprint matching, incremental estimation, and quantum navigation. The purpose and principles underlying each method are elucidated, followed by a rigorous examination and analysis of their respective technological strides. Subsequently, we encapsulate the unique attributes and strengths of high-precision indoor positioning technology in a concise summary. This thorough investigation aspires to be a catalyst in the progression and refinement of indoor positioning technologies. Lastly, we broach prospective trends, including diversification, intelligence, and popularization, and we speculate on a bright future ripe with opportunities for these technological innovations.

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.

Indoor positioning has attracted many attentions in recent years. Wi-Fi, geomagnetic and inertial sensors are commonly used sources to reconstruct pedestrian's trajectory. But the error drift of inertial sensors and the labor-intensive fingerprints construction constrain their popularity. The purpose of this paper is to design a Wi-Fi, geomagnetic and pedestrian dead-reckoning (PDR) composed indoor positioning system which is accurate, practical and less labor-intensive. In order to achieve this goal, we design an indoor positioning system based on walking-surveyed Wi-Fi fingerprint and corner reference trajectory-geomagnetic database (CRTDB). Firstly, we propose a trajectory optimization algorithm which makes use of landmark observations to optimize historical positions of PDR by means of Gauss-Newton algorithm. Next, we construct the walking-surveyed Wi-Fi fingerprint database and the CRTDB using the optimized PDR trajectories. Secondly, we propose a Dynamic Time Warping (DTW) and Pearson Correlation Coefficient (PCC) combined CRTDB matching algorithm to find the coordinates of the observed corners. Thirdly, we propose a PDR, Wi-Fi fingerprint and CRTDB fused positioning system based on Kalman filtering (KF). Finally, we evaluate the performance and effectiveness of our proposed algorithms on two open datasets. The experimental results show that with our proposed CRTDB optimizing, the median error is improved by more than 33% and 27% respectively compared with that of PDR and Wi-Fi fusion.