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Special Issue "Indoor Wi-Fi Positioning: Techniques and Systems"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 31 October 2022 | Viewed by 6203

Special Issue Editor

Prof. Günther Retscher
E-Mail Website
Guest Editor
Department of geodesy and geoinformation, Technische Universitat Wien, 1040 Vienna, Austria
Interests: positioning and navigation with GNSS; location-based services; indoor and pedestrian navigation; applications of multi-sensor systems; smartphone positioning and sensor fusion

Special Issue Information

Wi-Fi (Wireless Fidelity) is one of the most widely used signals of opportunity for positioning and tracking mobile users. It is widely adopted for smartphone-based indoor positioning systems due to the availability of infrastructure already deployed for communications. A high number of Access Points (APs) of public and private networks exist, guaranteeing a high signal ubiquity. The Wi-Fi signals have therefore a high potential to be employed for numerous applications for localization and guidance. Localization is based either on direct measurements of the Received Signal Strength (RSS) of the surrounding Wi-Fi Access Points or on the measurement of the Round-Trip Time (RTT) between the mobile device and the Access Points. Localization methods, therefore, include lateration and fingerprinting algorithms. Depending on the positioning technique adopted, different levels of positioning accuracy are achievable. This Special Issue is addressed to researchers on all types of localization approaches and algorithms. A special emphasis will be given to novel approaches for RSS- and RTT-based positioning and their combination and integration. Moreover, sensor integration and fusion, especially in the case of smartphone positioning, is a major research direction. Original contributions focused on systems and technologies to enable a variety of indoor localization applications are welcome. This Special Issue will provide the opportunity to disseminate among the scientific community relevant and new contributions related to the widespread use of Wi-Fi, such as for mobile devices, for the localization of mobile users in indoor and transitional environments, and for Location-Based Services (LBS), as well as to algorithm developments for these applications and the use of new and traditional technologies based on Wi-Fi for indoor spaces.

Prof. Günther Retscher
Guest Editor

Manuscript Submission Information

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Keywords

  • Wireless Fidelity Wi-Fi
  • Received Signal Strength RSS
  • Round Trip Time RTT Measurements
  • Lateration
  • Location Fingerprinting
  • Combination of Techniques
  • Sensor Integration and Fusion
  • Positioning Algorithms
  • Indoor User Localization
  • Seamless Transition between Different Environments

Published Papers (7 papers)

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Research

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Article
WiFi FTM and UWB Characterization for Localization in Construction Sites
Sensors 2022, 22(14), 5373; https://doi.org/10.3390/s22145373 - 19 Jul 2022
Viewed by 430
Abstract
A high-precision location is becoming a necessity in the future Industry 4.0 applications that will come up in the near future. However, the construction sector remains particularly obsolete in the adoption of Industry 4.0 applications. In this work, we study the accuracy and [...] Read more.
A high-precision location is becoming a necessity in the future Industry 4.0 applications that will come up in the near future. However, the construction sector remains particularly obsolete in the adoption of Industry 4.0 applications. In this work, we study the accuracy and penetration capacity of two technologies that are expected to deal with future high-precision location services, such as ultra-wide band (UWB) and WiFi fine time measurement (FTM). For this, a measurement campaign has been performed in a construction environment, where UWB and WiFi-FTM setups have been deployed. The performance of UWB and WiFi-FTM have been compared with a prior set of indoors measurements. UWB seems to provide better ranging estimation in LOS conditions but it seems cancelled by reinforcement concrete for propagation and WiFi is able to take advantage of holes in the structure to provide location services. Moreover, the impact of fusion of location technologies has been assessed to measure the potential improvements in the construction scenario. Full article
(This article belongs to the Special Issue Indoor Wi-Fi Positioning: Techniques and Systems)
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Article
Indoor Localization Using Uncooperative Wi-Fi Access Points
Sensors 2022, 22(8), 3091; https://doi.org/10.3390/s22083091 - 18 Apr 2022
Cited by 1 | Viewed by 616
Abstract
Indoor localization using fine time measurement (FTM) round-trip time (RTT) with respect to cooperating Wi-Fi access points (APs) has been shown to work well and provide 1–2 m accuracy in both 2D and 3D applications. This approach depends on APs implementing the IEEE [...] Read more.
Indoor localization using fine time measurement (FTM) round-trip time (RTT) with respect to cooperating Wi-Fi access points (APs) has been shown to work well and provide 1–2 m accuracy in both 2D and 3D applications. This approach depends on APs implementing the IEEE 802.11-2016 (also known as IEEE 802.11mc) Wi-Fi standard (“two-sided” RTT). Unfortunately, the penetration of this Wi-Fi protocol has been slower than anticipated, perhaps because APs tend not to be upgraded as often as other kinds of electronics, in particular in large institutions—where they would be most useful. Recently, Google released Android 12, which also supports an alternative “one-sided” RTT method that will work with legacy APs as well. This method cannot subtract out the “turn-around” time of the signal, and so, produces distance estimates that have much larger offsets than those seen with two-sided RTT—and the results are somewhat less accurate. At the same time, this method makes possible distance measurements for many APs that previously could not be used. This increased accessibility can compensate for the decreased accuracy of individual measurements. We demonstrate here indoor localization using one-sided RTT with respect to legacy APs that do not support IEEE 802.11-2016. The accuracy achieved is 3–4 m in cluttered environments with few line-of-sight readings (and using only 20 MHz bandwidths). This is not as good as for two-sided RTT, where 1–2 m accuracy has been achieved (using 80 MHz bandwidths), but adequate for many applications A wider Wi-Fi channel bandwidth would increase the accuracy further. As before, Bayesian grid update is the preferred method for determining position and positional accuracy, but the observation model now is different from that for two-sided RTT. As with two-sided RTT, the probability of an RTT measurement below the true distance is very low, but, in the other direction, the range of measurements for a given distance can be much wider (up to well over twice the actual distance). We describe methods for formulating useful observation models. As with two-sided RTT, the offset or bias in distance measurements has to be subtracted from the reported measurements. One difference is that here, the offsets are large (typically in the 2400–2700 m range) because of the “turn-around time” of roughly 16 μs (i.e., about two orders of magnitude larger than the time of flight one is attempting to measure). We describe methods for estimating these offsets and for minimizing the effort required to do so when setting up an installation with many APs. Full article
(This article belongs to the Special Issue Indoor Wi-Fi Positioning: Techniques and Systems)
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Article
WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning
Sensors 2021, 21(21), 7020; https://doi.org/10.3390/s21217020 - 23 Oct 2021
Cited by 4 | Viewed by 904
Abstract
High-precision indoor localisation is becoming a necessity with novel location-based services that are emerging around 5G. The deployment of high-precision indoor location technologies is usually costly due to the high density of reference points. In this work, we propose the opportunistic fusion of [...] Read more.
High-precision indoor localisation is becoming a necessity with novel location-based services that are emerging around 5G. The deployment of high-precision indoor location technologies is usually costly due to the high density of reference points. In this work, we propose the opportunistic fusion of several different technologies, such as ultra-wide band (UWB) and WiFi fine-time measurement (FTM), in order to improve the performance of location. We also propose the use of fusion with cellular networks, such as LTE, to complement these technologies where the number of reference points is under-determined, increasing the availability of the location service. Maximum likelihood estimation (MLE) is presented to weight the different reference points to eliminate outliers, and several searching methods are presented and evaluated for the localisation algorithm. An experimental setup is used to validate the presented system, using UWB and WiFi FTM due to their incorporation in the latest flagship smartphones. It is shown that the use of multi-technology fusion in trilateration algorithm remarkably optimises the precise coverage area. In addition, it reduces the positioning error by over-determining the positioning problem. This technique reduces the costs of any network deployment oriented to location services, since a reduced number of reference points from each technology is required. Full article
(This article belongs to the Special Issue Indoor Wi-Fi Positioning: Techniques and Systems)
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Article
Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of Trajectories
Sensors 2021, 21(17), 5776; https://doi.org/10.3390/s21175776 - 27 Aug 2021
Viewed by 888
Abstract
In a Wi-Fi indoor positioning system (IPS), the performance of the IPS depends on the channel state information (CSI), which is often limited due to the multipath fading effect, especially in indoor environments involving multiple non-line-of-sight propagation paths. In this paper, we propose [...] Read more.
In a Wi-Fi indoor positioning system (IPS), the performance of the IPS depends on the channel state information (CSI), which is often limited due to the multipath fading effect, especially in indoor environments involving multiple non-line-of-sight propagation paths. In this paper, we propose a novel IPS utilizing trajectory CSI observed from predetermined trajectories instead of the CSI collected at each stationary location; thus, the proposed method enables all the CSI along each route to be continuously encountered in the observation. Further, by using a generative adversarial network (GAN), which helps enlarge the training dataset, the cost of trajectory CSI collection can be significantly reduced. To fully exploit the trajectory CSI’s spatial and temporal information, the proposed IPS employs a deep learning network of a one-dimensional convolutional neural network–long short-term memory (1DCNN-LSTM). The proposed IPS was hardware-implemented, where digital signal processors and a universal software radio peripheral were used as a modem and radio frequency transceiver, respectively, for both access point and mobile device of Wi-Fi. We verified that the proposed IPS based on the trajectory CSI far outperforms the state-of-the-art IPS based on the CSI collected from stationary locations through extensive experimental tests and computer simulations. Full article
(This article belongs to the Special Issue Indoor Wi-Fi Positioning: Techniques and Systems)
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Article
Transfer Learning for Wireless Fingerprinting Localization Based on Optimal Transport
Sensors 2020, 20(23), 6994; https://doi.org/10.3390/s20236994 - 07 Dec 2020
Cited by 2 | Viewed by 822
Abstract
Wireless fingerprinting localization (FL) systems identify locations by building radio fingerprint maps, aiming to provide satisfactory location solutions for the complex environment. However, the radio map is easy to change, and the cost of building a new one is high. One research focus [...] Read more.
Wireless fingerprinting localization (FL) systems identify locations by building radio fingerprint maps, aiming to provide satisfactory location solutions for the complex environment. However, the radio map is easy to change, and the cost of building a new one is high. One research focus is to transfer knowledge from the old radio maps to a new one. Feature-based transfer learning methods help by mapping the source fingerprint and the target fingerprint to a common hidden domain, then minimize the maximum mean difference (MMD) distance between the empirical distributions in the latent domain. In this paper, the optimal transport (OT)-based transfer learning is adopted to directly map the fingerprint from the source domain to the target domain by minimizing the Wasserstein distance so that the data distribution of the two domains can be better matched and the positioning performance in the target domain is improved. Two channel-models are used to simulate the transfer scenarios, and the public measured data test further verifies that the transfer learning based on OT has better accuracy and performance when the radio map changes in FL, indicating the importance of the method in this field. Full article
(This article belongs to the Special Issue Indoor Wi-Fi Positioning: Techniques and Systems)
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Article
Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients K-Nearest Neighbor
Sensors 2020, 20(18), 5416; https://doi.org/10.3390/s20185416 - 21 Sep 2020
Cited by 9 | Viewed by 1004
Abstract
The complex indoor environment makes the use of received fingerprints unreliable as an indoor positioning and localization method based on fingerprint data. This paper proposes an adaptive multi-type fingerprint indoor positioning and localization method based on multi-task learning (MTL) and Weight Coefficients K [...] Read more.
The complex indoor environment makes the use of received fingerprints unreliable as an indoor positioning and localization method based on fingerprint data. This paper proposes an adaptive multi-type fingerprint indoor positioning and localization method based on multi-task learning (MTL) and Weight Coefficients K-Nearest Neighbor (WCKNN), which integrates magnetic field, Wi-Fi and Bluetooth fingerprints for positioning and localization. The MTL fuses the features of different types of fingerprints to search the potential relationship between them. It also exploits the synergy between the tasks, which can boost up positioning and localization performance. Then the WCKNN predicts another position of the fingerprints in a certain class determined by the obtained location. The final position is obtained by fusing the predicted positions using a weighted average method whose weights are the positioning errors provided by positioning error prediction models. Experimental results indicated that the proposed method achieved 98.58% accuracy in classifying locations with a mean positioning error of 1.95 m. Full article
(This article belongs to the Special Issue Indoor Wi-Fi Positioning: Techniques and Systems)
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Systematic Review
Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review
Sensors 2022, 22(12), 4622; https://doi.org/10.3390/s22124622 - 19 Jun 2022
Viewed by 653
Abstract
Nowadays, there are a multitude of solutions for indoor positioning, as opposed to standards for outdoor positioning such as GPS. Among the different existing studies on indoor positioning, the use of Wi-Fi signals together with Machine Learning algorithms is one of the most [...] Read more.
Nowadays, there are a multitude of solutions for indoor positioning, as opposed to standards for outdoor positioning such as GPS. Among the different existing studies on indoor positioning, the use of Wi-Fi signals together with Machine Learning algorithms is one of the most important, as it takes advantage of the current deployment of Wi-Fi networks and the increase in the computing power of computers. Thanks to this, the number of articles published in recent years has been increasing. This fact makes a review necessary in order to understand the current state of this field and to classify different parameters that are very useful for future studies. What are the most widely used machine learning techniques? In what situations have they been tested? How accurate are they? Have datasets been properly used? What type of Wi-Fi signals have been used? These and other questions are answered in this analysis, in which 119 papers are analyzed in depth following PRISMA guidelines. Full article
(This article belongs to the Special Issue Indoor Wi-Fi Positioning: Techniques and Systems)
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