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Indoor Positioning with Wireless Local Area Networks (WLAN)
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Indoor Positioning with Wireless Local Area Networks (WLAN)

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 10358

Special Issue Editor

Dr. Jukka Talvitie

E-Mail Website
Guest Editor
Department of Electrical Engineering, Tampere University, Tampere, Finland
Interests: radio positioning; 5G positioning; location-aware communications; signal processing for communications; radio signal waveform design

Special Issue Information

Dear Colleagues,

Indoor positioning systems enable the utilization of location-based services in areas where Global Navigation Satellite Systems (GNSSs) and, for example, mobile-network-based positioning systems are inadequate to provide the desired positioning performance. Potential applications of indoor positioning are spread among a wide variety of use cases and scenarios, such as office buildings, hospitals, multistory shopping malls and airports. Moreover, in the majority of the considered indoor positioning use cases, buildings have a substantial Wireless Local Area Network (WLAN) coverage, which makes WLAN-based positioning one of the most promising approach for practical indoor positioning systems.

Depending on the use case, WLAN-based positioning systems can utilize conventional positioning technologies, including measurements, such as Received Signal Strength (RSS), Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA) and Angle-Of-Arrical (AOA). However, due to challenging indoor radio propagation environments with significant multipath propagation and limited Line-Of-Sight (LOS) connectivity, many of the conventional positioning technologies require careful design in order to meet satisfactory positioning performance. In addition, WLAN-based positioning systems can be divided into two categories, where in one category the network is managed by the positioning service provider, in which case many network parameters, such as WLAN access point locations, are available for the positioning algorithms, while in the other category, the WLANs are used as signals of opportunity without any information on the underlying network parameters. In the latter case, fingerprinting-based positioning approaches have gained a lot of attention, as they can be widely applied for a variety of uses. Nonetheless, fingerprinting systems require efficient solutions for many practical issues—for example, collecting the fingerprints, managing and compressing large databases and handling measurement offsets between different devices.

Topics of interest include, but are not limited to, the following areas:

  • Positioning technologies: RSS, TOA, TDOA, AOA, RTT, fingerprinting
  • Tracking methods: Bayesian approaches, Kalman filters, particle filters
  • Sensor fusion: inertial sensors, magnetometer, barometer, etc.
  • Hybrid positioning solutions
  • Complementing 5G positioning with WLAN-based systems
  • Crowdsourcing methods for collecting fingerprints
  • Simultaneous Localization and Mapping (SLAM)
  • Fingerprint database compression
  • Machine learning for WLAN-based positioning
  • WLAN channel modeling for positioning
  • WLAN channel measurements for positioning
  • Seamless outdoor-to-indoor transition (e.g. from GNSS to WLAN)
  • Floor detection methods
  • 3D-positioning
  • Optimal network configuration and access point deployment for positioning
  • Location-based services
  • Security aspects of WLAN-based positioning

Dr. Jukka Talvitie
Guest Editor

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Keywords

  • WLAN positioning technologies
  • Tracking methods
  • Sensor fusion
  • Fingerprinting
  • Machine learning
  • Floor detection
  • 3D-positioning

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Published Papers (4 papers)

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15 pages, 3034 KiB  
Article
SLAM on the Hexagonal Grid
by Piotr Duszak
Sensors 2022, 22(16), 6221; https://doi.org/10.3390/s22166221 - 19 Aug 2022
Viewed by 2295
Abstract
Hexagonal grids have many advantages over square grids and could be successfully used in mobile robotics as a map representation. However, there is a lack of an essential algorithm, namely, SLAM (simultaneous localization and mapping), that would generate a map directly on the [...] Read more.
Hexagonal grids have many advantages over square grids and could be successfully used in mobile robotics as a map representation. However, there is a lack of an essential algorithm, namely, SLAM (simultaneous localization and mapping), that would generate a map directly on the hexagonal grid. In this paper, this issue is addressed. The solution is based on scan matching and solving the least-square problem with the Gauss–Newton formula, but it is modified with the Lagrange multiplier theorem. This is necessary to fulfill the constraints given by the manifold. The algorithm was tested in the synthetic environment and on a real robot and is entirely fully suitable for the presented task. It generates a very accurate map and generally has even better precision than the similar approach implemented on the square lattice. Full article
(This article belongs to the Special Issue Indoor Positioning with Wireless Local Area Networks (WLAN))
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18 pages, 4783 KiB  
Article
SICD: Novel Single-Access-Point Indoor Localization Based on CSI-MIMO with Dimensionality Reduction
by Yunwei Zhang, Weigang Wang, Chendong Xu, Jie Qin, Shujuan Yu and Yun Zhang
Sensors 2021, 21(4), 1325; https://doi.org/10.3390/s21041325 - 13 Feb 2021
Cited by 12 | Viewed by 2527
Abstract
With the rise of location-based services and the rapidly growing requirements related to their applications, indoor localization based on channel state information–multiple-input multiple-output (CSI-MIMO) has become an important research topic. However, indoor localization based on CSI-MIMO has some disadvantages, including noise and high [...] Read more.
With the rise of location-based services and the rapidly growing requirements related to their applications, indoor localization based on channel state information–multiple-input multiple-output (CSI-MIMO) has become an important research topic. However, indoor localization based on CSI-MIMO has some disadvantages, including noise and high data dimensions. To overcome the above drawbacks, we proposed a novel method of indoor localization based on CSI-MIMO, named SICD. For SICD, a novel localization fingerprint was first designed which can reflect the time–frequency and space–frequency characteristics of CSI-MIMO under a single access point (AP). To reduce the redundancy in the data of CSI-MIMO amplitude, we developed a data dimensionality reduction algorithm. Moreover, by leveraging a log-normal distribution, we calculated the conditional probability of the naive Bayes classifier, which was used to predict the moving object’s location. Compared with other state-of-the-art methods, the results of the experiment confirm that the SICD effectively improves localization accuracy. Full article
(This article belongs to the Special Issue Indoor Positioning with Wireless Local Area Networks (WLAN))
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11 pages, 394 KiB  
Communication
Constrained L1-Norm Minimization Method for Range-Based Source Localization under Mixed Sparse LOS/NLOS Environments
by Chengwen He, Yunbin Yuan and Bingfeng Tan
Sensors 2021, 21(4), 1321; https://doi.org/10.3390/s21041321 - 13 Feb 2021
Cited by 4 | Viewed by 2022
Abstract
Under mixed sparse line-of-sight/non-line-of-sight (LOS/NLOS) conditions, how to quickly achieve high positioning accuracy is still a challenging task and a critical problem in the last dozen years. To settle this problem, we propose a constrained L1 norm minimization method which can reduce the [...] Read more.
Under mixed sparse line-of-sight/non-line-of-sight (LOS/NLOS) conditions, how to quickly achieve high positioning accuracy is still a challenging task and a critical problem in the last dozen years. To settle this problem, we propose a constrained L1 norm minimization method which can reduce the effects of NLOS bias for improve positioning accuracy and speed up calculation via an iterative method. We can transform the TOA-based positioning problem into a sparse optimization one under mixed sparse LOS/NLOS conditions if we consider NLOS bias as outliers. Thus, a relatively good method to deal with sparse localization problem is L1 norm. Compared with some existing methods, the proposed method not only has the advantages of simple and intuitive principle, but also can neglect NLOS status and corresponding NLOS errors. Experimental results show that our algorithm performs well in terms of computational time and positioning accuracy. Full article
(This article belongs to the Special Issue Indoor Positioning with Wireless Local Area Networks (WLAN))
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Other

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13 pages, 995 KiB  
Letter
Robust Indoor Localization Methods Using Random Forest-Based Filter against MAC Spoofing Attack
by DongHyun Ko, Seok-Hwan Choi, Sungyong Ahn and Yoon-Ho Choi
Sensors 2020, 20(23), 6756; https://doi.org/10.3390/s20236756 - 26 Nov 2020
Cited by 2 | Viewed by 2462
Abstract
With the development of wireless networks and mobile devices, interest on indoor localization systems (ILSs) has increased. In particular, Wi-Fi-based ILSs are widely used because of the good prediction accuracy without additional hardware. However, as the prediction accuracy decreases in environments with natural [...] Read more.
With the development of wireless networks and mobile devices, interest on indoor localization systems (ILSs) has increased. In particular, Wi-Fi-based ILSs are widely used because of the good prediction accuracy without additional hardware. However, as the prediction accuracy decreases in environments with natural noise, some studies were conducted to remove it. So far, two representative methods, i.e., the filtering-based method and deep learning-based method, have shown a significant effect in removing natural noise. However, the prediction accuracy of these methods severely decreased under artificial noise caused by adversaries. In this paper, we introduce a new media access control (MAC) spoofing attack scenario injecting artificial noise, where the prediction accuracy of Wi-Fi-based indoor localization system significantly decreases. We also propose a new deep learning-based indoor localization method using random forest(RF)-filter to provide the good prediction accuracy under the new MAC spoofing attack scenario. From the experimental results, we show that the proposed indoor localization method provides much higher prediction accuracy than the previous methods in environments with artificial noise. Full article
(This article belongs to the Special Issue Indoor Positioning with Wireless Local Area Networks (WLAN))
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