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Electronics
Special Issues
Advanced Indoor Localization Technologies: From Theory to Application
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Advanced Indoor Localization Technologies: From Theory to Application

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 15 February 2026 | Viewed by 782

Special Issue Editor

Dr. Jung Min Pak

E-Mail Website
Guest Editor
Department of AI Data Engineering, Korea National University of Transportation, Uiwang-si 16106, Republic of Korea
Interests: state estimation; localization; target tracking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Indoor localization systems are systems that provide information on the positions of human, robot, and equipment to the users, and they have been used in various fields, such as factories, construction sites, and hospitals. In the future, indoor localization systems will be used for more diverse fields, and more advanced localization systems will be needed. In recent years, the emerging Internet of Things (IoT) has accelerated research on advanced indoor localization technologies because it requires accurate and reliable position information of various digital devices in cluttered indoor spaces. Indoor localization systems typically use measurements of wireless signals, such as WiFi and UWB, and they are related to the fields of wireless communications. To compute the coordinates of targets, localization systems use some mathematical tools, such as least square methods or state estimation algorithms, which are related to the mathematical/control theory. Since the indoor localization systems are related to the recently emerging technologies, we need to investigate state-of-the-art localization algorithms and their applications.

This Special Issue focuses on the advanced indoor localization systems, from theory to application. The specific topics of interest can include but are not limited to the following:

  • Wireless communication technologies for advanced indoor localization systems;
  • Advanced indoor localization algorithms;
  • Applications of indoor localization systems.

Dr. Jung Min Pak
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • indoor localization
  • localization
  • positioning

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Published Papers (2 papers)

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Research

18 pages, 2727 KB  
Article
Heterogeneous Graph Neural Network for WiFi RSSI-Based Indoor Floor Classification
by Houjin Lu and Seung-Hoon Hwang
Electronics 2025, 14(24), 4845; https://doi.org/10.3390/electronics14244845 - 9 Dec 2025
Viewed by 80
Abstract
Accurate indoor floor classification is essential for wireless positioning systems. However, the performance of conventional received signal strength indictor (RSSI)-based fingerprinting approaches is often limited by signal fluctuations and insufficient feature representation. To address these challenges, this paper introduces a heterogeneous graph neural [...] Read more.
Accurate indoor floor classification is essential for wireless positioning systems. However, the performance of conventional received signal strength indictor (RSSI)-based fingerprinting approaches is often limited by signal fluctuations and insufficient feature representation. To address these challenges, this paper introduces a heterogeneous graph neural network (GNN) framework that models WiFi signals using two types of nodes: reference points and Media Access Control (MAC) address. The edges between reference points and MAC addresses are weighted by normalized RSSI values, allowing the model to capture signal strength interactions and perform relation-aware message passing. Through this graph-based representation, the model can learn spatial and signal dependencies more effectively than conventional vector-based approaches. The proposed model was extensively evaluated under both benchmark and practical settings. On small-scale datasets, it achieved performance comparable to that of a conventional convolutional neural network trained on large-scale datasets, confirming its effectiveness with limited samples. In addition, the proposed model consistently outperformed other models under noisy conditions, achieving 93.88% accuracy on the widely used UJIIndoorLoc dataset and 97.3% accuracy in real-time experiments conducted at a test site. These values are significantly higher than those achieved using conventional machine learning (ML) baselines, highlighting the ability of the proposed model to handle real-world signal variations. These findings highlight that the heterogeneous GNN effectively captures spatial and signal-level dependencies, offering a robust and scalable solution for accurate indoor floor classification. Overall, this work presents a promising pathway for improving the performance and reliability of future wireless positioning systems. Full article
(This article belongs to the Special Issue Advanced Indoor Localization Technologies: From Theory to Application)
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21 pages, 1297 KB  
Article
Neural Network-Aided Hybrid Particle/FIR Filter for Indoor Localization Using Wireless Sensor Networks
by Jung Min Pak
Electronics 2025, 14(21), 4346; https://doi.org/10.3390/electronics14214346 - 6 Nov 2025
Viewed by 297
Abstract
Indoor localization based on range measurements in wireless sensor networks involves nonlinear measurement models and is susceptible to non-Gaussian noise, which is associated with complex indoor environments. While particle filters (PFs) are well-suited to such systems, they suffer from sample impoverishment, whereby a [...] Read more.
Indoor localization based on range measurements in wireless sensor networks involves nonlinear measurement models and is susceptible to non-Gaussian noise, which is associated with complex indoor environments. While particle filters (PFs) are well-suited to such systems, they suffer from sample impoverishment, whereby a diminishing sample diversity leads to failures under various conditions. Hence, this paper proposes a novel hybrid localization algorithm that combines a PF, a finite impulse response (FIR) filter, and an artificial neural network. In the proposed algorithm, the PF serves as the main filter for localization because it performs excellently in nonlinear, non-Gaussian systems under normal operation. The neural network is trained to classify whether the system is operating normally or experiencing a failure, based on estimation results from the PF. If a PF failure is detected by the network, the assisting FIR filter is activated to recover the PF from failures. The localization accuracy and reliability of the proposed neural network-aided hybrid particle/FIR filter are confirmed via comparisons with existing algorithms. Full article
(This article belongs to the Special Issue Advanced Indoor Localization Technologies: From Theory to Application)
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