AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

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

Human Crowdsourcing Data for Indoor Location Applied to Ambient Assisted Living Scenarios

In the last decades, the rise of life expectancy has accelerated the demand for new technological solutions to provide a longer life with improved quality. One of the major areas of the Ambient Assisted Living aims to monitor the elderly location indoors. For this purpose, indoor positioning systems are valuable tools and can be classified depending on the need of a supporting infrastructure. Infrastructure-based systems require the investment on expensive equipment and existing infrastructure-free systems, although rely on the pervasively available characteristics of the buildings, present some limitations regarding the extensive process of acquiring and maintaining fingerprints, the maps that store the environmental characteristics to be used in the localisation phase. These problems hinder indoor positioning systems to be deployed in most scenarios. To overcome these limitations, an algorithm for the automatic construction of indoor floor plans and environmental fingerprints is proposed. With the use of crowdsourcing techniques, where the extensiveness of a task is reduced with the help of a large undefined group of users, the algorithm relies on the combination ofmultiple sources of information, collected in a non-annotated way by common smartphones. The crowdsourced data is composed by inertial sensors, responsible for estimating the users’ trajectories, Wi-Fi radio and magnetic field signals. Wi-Fi radio data is used to cluster the trajectories into smaller groups, each corresponding to specific areas of the building. Distance metrics applied to magnetic field signals are used to identify geomagnetic similarities between different users’ trajectories. The building’s floor plan is then automatically created, which results in fingerprints labelled with physical locations. Experimental results show that the proposed algorithm achieved comparable floor plan and fingerprints to those acquired manually, allowing the conclusion that is possible to automate the setup process of infrastructure-free systems. With these results, this solution can be applied in any fingerprinting-based indoor positioning system

Improving fingerprint-based positioning by using IEEE 802.11mc FTM/RTT observables

Received signal strength (RSS) has been one of the most used observables for location purposes due to its availability at almost every wireless device. However, the volatile nature of RSS tends to yield to non-reliable location solutions. IEEE 802.11mc enabled the use of the round trip time (RTT) for positioning, which is expected to be a more consistent observable for location purposes. This approach has been gaining support from several companies such as Google, which introduced that feature in the Android O.S. As a result, RTT estimation is now available in several recent off-the-shelf devices, opening a wide range of new approaches for computing location. However, RTT has been traditionally addressed to multilateration solutions. Few works exist that assess the feasibility of the RTT as an accurate feature in positioning methods based on classification algorithms. An attempt is made in this paper to fill this gap by investigating the performance of several classification models in terms of accuracy and positioning errors. The performance is assessed using different AP layouts, distinct AP vendors, and different frequency bands. The accuracy and precision of the RTT-based position estimation is always better than the one obtained with RSS in all the studied scenarios, and especially when few APs are available. In addition, all the considered ML algorithms perform pretty well. As a result, it is not necessary to use more complex solutions (e.g., SVM) when simpler ones (e.g., nearest neighbor classifiers) achieve similar results both in terms of accuracy and location error.This research was partially supported by MCIN/AEI/10.13039/ 501100011033 and ERDF “A way of making Europe” under grant PGC2018-099945-BI00, and by the European GNSS Agency (GSA) under grant GSA/GRANT/04/2019/BANSHEEPeer ReviewedPostprint (published version

A Fast-rate WLAN Measurement Tool for Improved Miss-rate in Indoor Navigation

Recently, location-based services (LBS) have steered attention to indoor positioning systems (IPS). WLAN-based IPSs relying on received signal strength (RSS) measurements such as fingerprinting are gaining popularity due to proven high accuracy of their results. Typically, sets of RSS measurements at selected locations from several WLAN access points (APs) are used to calibrate the system. Retrieval of such measurements from WLAN cards are commonly at one-Hz rate. Such measurement collection is needed for offline radio-map surveying stage which aligns fingerprints to locations, and for online navigation stage, when collected measurements are associated with the radio-map for user navigation. As WLAN network is not originally designed for positioning, an RSS measurement miss could have a high impact on the fingerprinting system. Additionally, measurement fluctuations require laborious signal processing, and surveying process can be very time consuming. This paper proposes a fast-rate measurement collection method that addresses previously mentioned problems by achieving a higher probability of RSS measurement collection during a given one-second window. This translates to more data for statistical processing and faster surveying. The fast-rate collection approach is analyzed against the conventional measurement rate in a proposed testing methodology that mimics real-life scenarios related to IPS surveying and online navigation

Sensor fusion of IMU and BLE using a well-condition triangle approach for BLE positioning

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesGPS has been a de-facto standard for outdoor positioning. For indoor positioning different systems exist. But there is no general solution to fit all situations. A popular choice among service provider is BLE-based IPS. BLE-has low cost, low power consumption, and tit is are compatible with newer smartphones. These factors make it suitable for mass market applications with an estimated market of 10 billion USD by 2020. Although, BLEbased IPS have advantages over its counterparts, it has not solved the position accuracy problem yet. More research is needed to meet the position accuracy required for indoor LBS. In this thesis, two ways for accuracy improvement were tested i) a new algorithm for BLE-based IPS was proposed and ii) fusion of BLE position estimates with IMU position estimates was implemented. The first way exploits a concept from control survey called well-conditioned triangle. Theoretically, a well-conditioned triangle is an equilateral triangle but for in practice, triangles whose angles are greater than 30° and less than 120° are considered well-conditioned. Triangles which do not satisfy well-condition are illconditioned. An estimated position has the least error if the geometry from which it is estimated satisfy well-condition. Ill-conditioned triangle should not be used for position estimation. The proposed algorithm checked for well-condition among the closest detected beacons and output estimates only when the beacons geometry satisfied well-condition. The proposed algorithm was compared with weighted centroid (WC) algorithm. Proposed algorithm did not improve on the accuracy but the variance in error was highly reduced. The second way tested was fusion of BLE and IMU using Kálmán filter. Fusion generally gives better results but a noteworthy result from fusion was that the position estimates during turns were accurate. When used separately, both BLE and IMU estimates showed errors in turns. Fusion with IMU improved the accuracy. More research is required to improve accuracy of BLE-based IPS. Reproducibility self-assessment (https://osf.io/j97zp/): 2, 2, 2, 1, 2 (input data, prepossessing, methods, computational environment, results)

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

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.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001