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Advanced Indoor Localization Technologies: From Theory to Application

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

Deadline for manuscript submissions: closed (15 February 2026) | Viewed by 3484

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Dr. Jung Min Pak

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Guest Editor

Department of AI Data Engineering, Korea National University of Transportation, Uiwang-si 16106, Republic of Korea
Interests: state estimation; localization; target tracking
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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

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Keywords

indoor localization
localization
positioning

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Published Papers (6 papers)

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Research

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31 pages, 2531 KB

Open AccessArticle

AI-Based Indoor Localization Using Virtual Anchors in Combination with Wake-Up Receiver Nodes

by Sirine Chiboub, Aziza Chabchoub, Rihab Souissi, Salwa Sahnoun, Ahmed Fakhfakh and Faouzi Derbel

Electronics 2026, 15(3), 584; https://doi.org/10.3390/electronics15030584 - 29 Jan 2026

Viewed by 251

Abstract

Accurate indoor localization is essential for navigation, monitoring, and industrial applications, especially in environments with Non-line of sight (NLOS) conditions. An indoor positioning system consists of fixed physical nodes, referred to as anchors, which serve as reference nodes with known locations, and entities that could be persons or objects that are also equipped with a node, referred to as targets, whose positions are estimated based on signal measurements exchanged with the surrounding anchors. Although RSSI is widely used due to hardware simplicity, its performance is often affected by signal degradation, multipath propagation, and environmental interference. To address this limitation, this work aims to develop an indoor positioning system, especially in wide areas with a minimal number of physical anchors, while maintaining high positioning accuracy and low latency. The proposed approach integrates VA, RSSI-based multilateration, and ML as a tool to refine and improve positioning accuracy, where ML models are used to predict the VA features and subsequently predict the corresponding distances. In addition, the system relies on energy-efficient WuRx nodes, which ensure a low power consumption and support on-demand communication. The study area covers two distinct floors with a total area of 366.9 m², covered using only four physical anchors. Two studies were performed, the offline and the online, in order to evaluate the proposed system under both the theoretical performance and real implementation conditions. In the offline phase, hexagonal and rectangular grid architectures were compared using multiple machine learning models under varying numbers of virtual anchors. By comparing different architectures and machine learning models, the rectangular grid with 10 virtual anchors combined with the XGBoost model achieved the best performance, resulting in an RMSE of

1.49 m

with a processing time of approximately

0.15 s

. The online evaluation confirmed the performance of the proposed system, achieving an RMSE of

2.48 m

. Full article

(This article belongs to the Special Issue Advanced Indoor Localization Technologies: From Theory to Application)

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20 pages, 1495 KB

Open AccessArticle

Recurrent Neural Networks with Attention for Indoor Localization in 5G: Evaluation on the xG-Loc Dataset

by Milton Soria, Sleiter Ramos-Sanchez, Jinmi Lezama and Alberto M. Coronado

Electronics 2026, 15(3), 575; https://doi.org/10.3390/electronics15030575 - 28 Jan 2026

Viewed by 304

Abstract

Accurate indoor localization in 5G remains challenging due to multipath propagation, signal blockage, and limited bandwidth in frequency range 1 (FR1). This study evaluates attention-based recurrent neural networks for two-dimensional user equipment (UE) localization using only positioning reference signal (PRS) magnitude data. We compare five models on the xG-Loc dataset (InF-DH scenario at 3.5 GHz, 5 MHz bandwidth): a simple GRU (M1), a deeper GRU with dropout (M2), a GRU optimized via Optuna (M3), a stacked GRU with multi-head attention (M4), and a bidirectional GRU with attention (M5). Model performance is quantified using the area above the cumulative distribution function (CDF) curve (AAC) metric, where lower values indicate better localization accuracy. Attention-based models significantly outperform baselines, and M4 achieves the lowest AAC of 6.71 (17% reduction versus M1’s 8.09), while M5 attains an AAC of 6.90. Statistical analysis confirms that M4 and M5 significantly outperform M3 (ANOVA, p < 0.000001). Optimal performance emerges with moderate numbers of time steps (TS ≈ 500 to 2500), with performance plateauing and degrading at higher values. These findings demonstrate that attention mechanisms substantially enhance 5G indoor localization accuracy using only PRS magnitudes, and that automated hyperparameter optimization improves model robustness. Full article

(This article belongs to the Special Issue Advanced Indoor Localization Technologies: From Theory to Application)

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20 pages, 3704 KB

Open AccessArticle

Accurate Position and Orientation Estimation for UWB-Only Systems Using a Single Dual-Antenna Module

by Che Zhang, Yan Li and Peng Han

Electronics 2026, 15(2), 369; https://doi.org/10.3390/electronics15020369 - 14 Jan 2026

Viewed by 324

Abstract

This paper proposes a complete cascade pipeline for accurate position and orientation estimation using a single dual-antenna UWB module. First, an extended Kalman filter (EKF) fuses distance measurements from multiple anchors to estimate the agent’s position. The estimated position is then used to derive orientation. To overcome the critical challenge of front–back ambiguity in orientation estimation, we introduce a novel method that integrates a multi-hypothesis testing (MHT) framework with a circular likelihood metric (CLM). This method enumerates all feasible angle of arrival (AoA) hypotheses via MHT and assesses their consistency using the CLM, thereby selecting the most probable hypothesis to resolve ambiguity. Comparative simulations demonstrate that this “position-first, orientation-later” cascade enhances robustness over joint optimization by preventing the propagation of AoA noise to the position estimates. Extensive evaluations, including high-precision rotary table experiment and real-world field trials, validate the system’s efficacy in providing precise location and heading information. This work delivers a complete, low-cost, and robust solution for autonomous navigation in challenging environments. Full article

(This article belongs to the Special Issue Advanced Indoor Localization Technologies: From Theory to Application)

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18 pages, 2727 KB

Open AccessEditor’s ChoiceArticle

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 464

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 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

Open AccessArticle

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 473

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 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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Review

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55 pages, 1023 KB

Open AccessReview

Machine Learning Integration in Ultra-Wideband-Based Indoor Positioning Systems: A Comprehensive Review

by Juan Carlos Santamaria-Pedrón, Rafael Berkvens, Ignacio Miralles, Carlos Reaño and Joaquín Torres-Sospedra

Electronics 2026, 15(1), 181; https://doi.org/10.3390/electronics15010181 - 30 Dec 2025

Viewed by 1148

Abstract

Ultra-Wideband (UWB) technology enables centimeter-level indoor positioning, but it remains highly sensitive to channel dynamics, multipath and Non-Line-of-Sight (NLoS) propagation. Recent studies increasingly apply Machine Learning (ML) methods to address these issues by modeling nonlinear channel behavior and mitigating ranging bias. This paper presents a comprehensive review and provides a critical synthesis of 169 research works published between 2020 and 2024, offering an integrated overview of how ML techniques are incorporated into UWB-based Indoor Positioning Systems (IPSs). The studies are grouped according to their functional objective, learning algorithm, network architecture, evaluation metrics, dataset, and experimental setting. The results indicate that most approaches apply ML to channel classification and ranging error mitigation, with Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and hybrid CNN–Long Short-Term Memory (LSTM) architectures being among the most common choices due to their ability to capture spatial and temporal patterns in the Channel Impulse Response (CIR). Despite the reported accuracy improvements, scalability and cross-environment generalization remain open challenges, largely due to the scarcity of public datasets and the lack of standardized evaluation protocols. Emerging research trends highlight growing interest in transfer learning, domain adaptation, and federated learning, along with lightweight and explainable models suitable for embedded and multi-sensor systems. Overall, this review summarizes the progress made in ML-driven UWB localization, identifies current gaps, and outlines promising directions toward more robust and generalizable indoor positioning frameworks. Full article

(This article belongs to the Special Issue Advanced Indoor Localization Technologies: From Theory to Application)

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