Multiple simultaneous Wi-Fi measurements in fingerprinting indoor positioning

The accuracy of fingerprinting-based positioning methods accuracy is limited by the fluctuations in the radio signal intensity mainly due to reflections, refractions, and multipath interference, among other factors. We consider that the fluctuations (often modelled as a Gaussian process for simplification purposes) can be minimized by exploiting the richness of multiple signals collected simultaneously through independent network interfaces. This paper introduces an analysis of Wi-Fi signals' statistics using simultaneous measurements which shows that RSSI values obtained from independent devices are not highly correlated. The low correlation between Wi-Fi interfaces might be exploited to improve the positioning accuracy. The validation of the proposed fingerprinting approach in a real scenario shows that the mean and maximum error in positioning can be reduced by more than 40% when five Wi-Fi interfaces are simultaneously used for fingerprinting.This work has been supported by COMPETE: POCI-01- 0145-FEDER-007043 and FCT—Fundação para a Ciência e Tecnologia within the scope of project UID/CEC/00319/2013, by the Portugal Incentive System for Research and Technological Development in the scope of the projects in co- promotion no 002814/2015 (iFACTORY 2015-2018), and by the José Castillejo mobility grant (CAS16/00072).info:eu-repo/semantics/publishedVersio

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

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

Collaborative Wi-Fi fingerprint training for indoor positioning

As the scope of location-based applications and services further reach into our everyday lives, the demand for more robust and reliable positioning becomes ever more important. However indoor positioning has never been a fully resolved issue due to its complexity and necessity to adapt to different situations and environment. Inertial sensor and Wi-Fi signal integrated indoor positioning have become good solutions to overcome many of the problems. Yet there are still problems such as inertial heading drift, wireless signal fluctuation and the time required for training a Wi-Fi fingerprint database. The collaborative Wi-Fi fingerprint training (cWiDB) method proposed in this paper enables the system to perform inertial measurement based collaborative positioning or Wi-Fi fingerprinting alternatively according to the current situation. It also reduces the time required for training the fingerprint database. Different database training methods and different training data size are compared to demonstrate the time and data required for generating a reasonable database. Finally the fingerprint positioning result is compared which indicates that the cWiDB is able to achieve the same positioning accuracy as conventional training methods but with less training time and a data adjustment option enabled

Low-effort place recognition with WiFi fingerprints using deep learning

Using WiFi signals for indoor localization is the main localization modality of the existing personal indoor localization systems operating on mobile devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals are usually available indoors and can provide rough initial position estimate or can be used together with other positioning systems. Currently, the best solutions rely on filtering, manual data analysis, and time-consuming parameter tuning to achieve reliable and accurate localization. In this work, we propose to use deep neural networks to significantly lower the work-force burden of the localization system design, while still achieving satisfactory results. Assuming the state-of-the-art hierarchical approach, we employ the DNN system for building/floor classification. We show that stacked autoencoders allow to efficiently reduce the feature space in order to achieve robust and precise classification. The proposed architecture is verified on the publicly available UJIIndoorLoc dataset and the results are compared with other solutions

Ubiquitous Indoor Fine-Grained Positioning and Tracking: A Channel Response Perspective

The future of location-aided applications is shaped by the ubiquity of Internet-of-Things devices. As an increasing amount of commercial off-the-shelf radio devices support channel response collection, it is possible to achieve fine-grained position estimation at a relatively low cost. In this paper, we focus on the channel response-based positioning and tracking for various applications. We first give an overview of the state of the art (SOTA) of channel response-enabled localization, which is further classified into two categories, i.e., device-based and contact-free schemes. A taxonomy for these complementary approaches is provided concerning the involved techniques. Then, we present a micro-benchmark of channel response-based direct positioning and tracking for both device-based and contact-free schemes. Finally, some practical issues for real-world applications and future research opportunities are pointed out.Comment: 13th International Conference on Indoor Positioning and Indoor Navigatio

Real-world deployment of low-cost indoor positioning systems for industrial applications

The deployment of an Indoor Position System (IPS) in the real-world raised many challenges, such as installation of infrastructure, the calibration process or modelling of the building's floor plan. For Wi-Fi-based IPSs, deployments often require a laborious and time-consuming site survey to build a Radio Map (RM), which tends to become outdated over time due to several factors. In this paper, we evaluate different deployment methods of a Wi-Fi-based IPS in an industrial environment. The proposed solution works in scenarios with different space restrictions and automatically builds a RM using industrial vehicles in operation. Localization and tracking of industrial vehicles, equipped with low-cost sensors, is achieved with a particle filter, which combines Wi-Fi measurements with heading and displacement data. This allows to automatically annotate and add new samples to a RM, named vehicle Radio Map (vRM), without human intervention. In industrial environments, vRMs can be used with Wi-Fi fingerprinting to locate human operators, industrial vehicles, or other assets, allowing to improve logistics, monitoring of operations, and safety of operators. Experiments in an industrial building show that the proposed solution is capable of automatically building a high-quality vRM in different scenarios, i.e., considering a complete floor plan, a partial floor plan or without a floor plan. Obtained results revealed that vRMs can be used in Wi-Fi fingerprinting with better accuracy than a traditional RM. Sub-meter accuracies were obtained for an industrial vehicle prototype after deployment in a real building.This work was supported in part by the Fundacao para a Ciencia e Tecnologia-FCT through the Research and Development Units Project Scope under Grant UIDB/00319/2020 and in part by the Ph.D. Fellowship under Grant PD/BD/137401/2018. The associate editor coordinating the review of this article and approving it for publication was Prof. Masanori Sugimoto

TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor Environments

Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle's initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles' weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%

Algorithm for Dynamic Fingerprinting Radio Map Creation Using IMU Measurements

While a vast number of location-based services appeared lately, indoor positioning solutions are developed to provide reliable position information in environments where traditionally used satellite-based positioning systems cannot provide access to accurate position estimates. Indoor positioning systems can be based on many technologies; however, radio networks and more precisely Wi-Fi networks seem to attract the attention of a majority of the research teams. The most widely used localization approach used in Wi-Fi-based systems is based on fingerprinting framework. Fingerprinting algorithms, however, require a radio map for position estimation. This paper will describe a solution for dynamic radio map creation, which is aimed to reduce the time required to build a radio map. The proposed solution is using measurements from IMUs (Inertial Measurement Units), which are processed with a particle filter dead reckoning algorithm. Reference points (RPs) generated by the implemented dead reckoning algorithm are then processed by the proposed reference point merging algorithm, in order to optimize the radio map size and merge similar RPs. The proposed solution was tested in a real-world environment and evaluated by the implementation of deterministic fingerprinting positioning algorithms, and the achieved results were compared with results achieved with a static radio map. The achieved results presented in the paper show that positioning algorithms achieved similar accuracy even with a dynamic map with a low density of reference points

A multimodal Fingerprint-based Indoor Positioning System for airports

[EN] Indoor Localization techniques are becoming popular in order to provide a seamless indoor positioning system enhancing the traditional GPS service that is only suitable for outdoor environments. Though there are proprietary and costly approaches targeting high accuracy positioning, Wi-Fi and BLE networks are widely deployed in many public and private buildings (e.g. shopping malls, airports, universities, etc.). These networks are accessible through mobile phones resulting in an effective commercial off-the-self basic infrastructure for an indoor service. The obtained positioning accuracy is still being improved and there is on-going research on algorithms adapted for Wi-Fi and BLE and also for the particularities of indoor environments. This paper focuses not only on indoor positioning techniques, but also on a multimodal approach. Traditional proposals employ only one network technology whereas this paper integrates two different technologies in order to provide improved accuracy. It also sets the basis for combining (merging) additional technologies, if available. The initial results show that the positioning service performs better with a multimodal approach compared to individual (monomodal) approaches and even compared with Google¿s geolocation service in public spaces such as airports.This work was supported in part by the European Commission through the Door to Door Information for Airports and Airlines Project under Grant GA 635885 and in part by the European Commission through the Interoperability of Heterogeneous IoT Platforms Project under Grant 687283.Molina Moreno, B.; Olivares-Gorriti, E.; Palau Salvador, CE.; Esteve Domingo, M. (2018). A multimodal Fingerprint-based Indoor Positioning System for airports. IEEE Access. 6:10092-10106. https://doi.org/10.1109/ACCESS.2018.2798918S1009210106