The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future.

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.

Existing indoor positioning systems based on Wi-Fi or Bluetooth confront difficulties and problems, such as low positioning accuracy, unstable, and small-signal coverage. A system using an acoustic ranging signal is presented in this article, and it is available for almost all commercial off-the-shelf (COTS) smartphones. A solution was developed to detect the modulated signals transmitted by specially designed anchor stations at each consisting of a speaker. The detector is a two-stage algorithm based on a generalized cross correlation (GCC) method and a time-frequency-based feature extraction. The detected timestamps are converted to time-difference-of-arrivals (TDOAs) observables, which can be applied with a least-squares (LS) estimator to obtain the smartphone positions in static cases. Furthermore, an extended Kalman filter (EKF) is introduced to integrate data from inertial measurement units (IMUs) and acoustic TDOA ranging observables for estimating a better trajectory in dynamic cases. Experimental results show that the signal detection algorithm is accurate with a mean error of about 0.3-1 m in terms of ranging estimation. The proposed method offers a static positioning accuracy of better than 0.5 m. For dynamic tests, positioning accuracy of 0.5 m at 50% and 1.6 m at 95% is achieved with four anchors in the first scene. In the second scene with a large test site, it achieves a dynamic positioning accuracy of 0.5 m at 50% and 1.82 m at 95% with eight anchors, which is comparable with the positioning accuracy of the ultrawideband (UWB) technology.

The weighted K-nearest neighbor (WKNN) algorithm is the most commonly used algorithm for indoor localization. Traditional WKNN algorithms adopt received signal strength (RSS) spatial distance (usually Euclidean distance and Manhattan distance) to select reference points (RPs) for position determination. It may lead to inaccurate position estimation because the relationship of received signal strength and distance is exponential. To improve the position accuracy, this paper proposes an improved weighted K-nearest neighbor algorithm. The spatial distance and physical distance of RSS are used for RP selection, and a fusion weighted algorithm based on these two distances is used for position calculation. The experimental results demonstrate that the proposed algorithm outperforms traditional algorithms, such as K-nearest neighbor (KNN), Euclidean distance-based WKNN (E-WKNN), and physical distance-based WKNN (P-WKNN). Compared with the KNN, E-WKNN, and P-WKNN algorithms, the positioning accuracy of the proposed method is improved by about 29.4%, 23.5%, and 20.7%, respectively. Compared with some recently improved WKNN algorithms, our proposed algorithm can also obtain a better positioning performance.

During the past decade, there has been an increasing interest in the audio-based indoor localization approach, which has benefited from the high precision, high privacy, and high security of the passive time difference of arrival positioning. However, the limited bandwidth and complexity in real-time ID encoding and decoding make it difficult to achieve wide-area (>1500 m ² ) localization in off-the-shelf smartphones. Plenty of previous work applied to a single region localization only with a small range. In this article, a hybrid acoustic signal transmission architecture based on frequency division multiple access, time division multiple access, and space division multiple access is perfected to cover the acoustic signal to a wider range. With the prior information of the Bluetooth and floor-plan, smartphones estimate which cell themselves are located in accurately. An enhanced particle filter algorithm, along with the microelectromechanical systems-based pedestrian dead reckoning and the floor-plan restraint, was proposed to make the smartphones tracking smarter and more robust. The proposed prototype system provided submeter-level localization accuracy and stable 1-Hz update rate in several typical wide indoor environments with less infrastructure, which demonstrates some values of reference and application owing to the work of this article.