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Indoor Localization Techniques Based on Wireless Communication
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Indoor Localization Techniques Based on Wireless Communication

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 3256

Special Issue Editor

Dr. Marios Raspopoulos

E-Mail Website
Guest Editor
INSPIRE Research Centre, University of Central Lancashire (UCLAN), Larnaca, Cyprus
Interests: telecommunications–mobile communications-5G communications; electronic positioning/localization/tracking of wireless devices; design and management of wireless telecommunication systems and networks; radio propagation/radio planning/wireless channel modelling; human exposure to electromagnetic radiation; information systems development; ubiquitous and pervasive computing/internet of things; responsibility in smart environments/technologies

Special Issue Information

Dear Colleagues,

Indoor localization has become a key enabling technology for smart environments, including smart buildings, Industry 4.0, healthcare, logistics, and immersive tools. As GNSS signals remain unreliable indoors, the scholarly community continues to advance wireless-based positioning and hybrid sensing methods to achieve high accuracy, robustness, and scalability in complex indoor spaces. Emerging wireless technologies, such as Wi-Fi, BLE, UWB, 5G/6G, radar, RFID, optical, acoustic, and magnetic field–based systems, combined with AI, sensor fusion, and context-awareness, are reshaping the capabilities of indoor positioning and navigation.

This Special Issue will highlight recent advances in, experimental findings on, and innovative applications in indoor localization techniques based on wireless communications. Particular focus is placed on indoor positioning, navigation, and tracking methods such as (but not limited to) the following:

  • AI- and ML-based systems;
  • Wireless sensor or cellular network-based positioning;
  • AoA-, TOA-, TOF-, and TDOA-based techniques;
  • Channel Impulse Response-based methods;
  • Cooperative localization systems;
  • Fingerprinting approaches;
  • Hybrid IMU and foot-mounted pedestrian navigation;
  • Magnetic field-based localization;
  • Inertial and hybrid tracking systems;
  • Radar-based localization (e.g., mmWave);
  • Simultaneous localization and mapping (SLAM);
  • 5G, 6G, and UWB positioning;
  • Sensor fusion algorithms;
  • Smartphone-based localization;
  • FTM and CSI-based Wi-Fi localization, and related topics.

Dr. Marios Raspopoulos
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. Sensors 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 2600 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

  • UWB positioning
  • indoor localization
  • radar-based localization
  • Wi-Fi localization
  • foot-mounted pedestrian navigation
  • cooperative localization systems
  • wireless sensor or cellular network-based positioning
  • simultaneous localization and mapping (SLAM)
  • smartphone-based localization

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Published Papers (3 papers)

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23 pages, 2536 KB  
Article
Axes Mapping and Sensor Fusion for Attitude-Unconstrained Pedestrian Dead Reckoning
by Constantina Isaia, Lingming Yu, Wenyu Cai and Michalis P. Michaelides
Sensors 2026, 26(6), 1968; https://doi.org/10.3390/s26061968 - 21 Mar 2026
Viewed by 1266
Abstract
Localization and navigation techniques have become fundamental for modern lives, while achieving accurate results indoors still remains a significant challenge. The widespread adoption of smart devices, and especially smartphones, has increased the need for accurate and robust pedestrian dead reckoning systems that operate [...] Read more.
Localization and navigation techniques have become fundamental for modern lives, while achieving accurate results indoors still remains a significant challenge. The widespread adoption of smart devices, and especially smartphones, has increased the need for accurate and robust pedestrian dead reckoning systems that operate in infrastructure-less environments. Pedestrian dead reckoning’s primary challenge is maintaining accuracy despite varying smartphone placements (attitudes) and the noisy, low-cost inertial measurements units. In this work, a comprehensive pedestrian dead reckoning framework is presented that integrates advanced step counting and heading estimation techniques. For step detection and counting, we propose a robust step counting algorithm that utilizes the optimum fusion of the raw IMU readings, i.e., accelerometer, linear accelerometer, gyroscope, and magnetometer readings, each broken down into three degrees of freedom for different body placements and walking speeds. Furthermore, to address the critical issue of heading estimation, we propose the heading estimation axis mapping (HEAT-MAP) algorithm, which dynamically adjusts the sensor axes in response to the smartphone’s orientation, ensuring a consistent coordinate frame and reducing heading drift. Moreover, to eliminate cumulative pedestrian dead reckoning errors, the system incorporates an adaptive weighted fusion mechanism with Wi-Fi fingerprinting. Experimental results demonstrate that this integrated system significantly improves the overall trajectory accuracy, providing a high-precision, attitude-unconstrained solution for real-time indoor pedestrian navigation. Full article
(This article belongs to the Special Issue Indoor Localization Techniques Based on Wireless Communication)
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47 pages, 2396 KB  
Article
Adaptive Multi-Stage Hybrid Localization for RIS-Aided 6G Indoor Positioning Systems: Combining Fingerprinting and Geometric Methods with Condition-Aware Fusion
by Iacovos Ioannou, Vasos Vassiliou and Marios Raspopoulos
Sensors 2026, 26(4), 1084; https://doi.org/10.3390/s26041084 - 7 Feb 2026
Cited by 1 | Viewed by 602
Abstract
Reconfigurable intelligent surfaces (RISs) represent a paradigm shift in wireless communications, offering unprecedented control over electromagnetic wave propagation for next-generation 6G networks. This paper presents a comprehensive framework for high-precision indoor localization exploiting cooperative multi-RIS deployments. We introduce the adaptive multi-stage hybrid localization [...] Read more.
Reconfigurable intelligent surfaces (RISs) represent a paradigm shift in wireless communications, offering unprecedented control over electromagnetic wave propagation for next-generation 6G networks. This paper presents a comprehensive framework for high-precision indoor localization exploiting cooperative multi-RIS deployments. We introduce the adaptive multi-stage hybrid localization (AMSHL) algorithm, a novel approach that strategically combines fingerprinting-based and geometric time-difference-of-arrival (TDoA) methods through condition-aware adaptive fusion. The proposed framework employs a 4-RIS cooperative architecture with strategically positioned panels on room walls, enabling comprehensive spatial coverage and favorable geometric diversity. AMSHL incorporates five key innovations: (1) a hybrid fingerprint database combining received signal strength indicator (RSSI) and TDoA features for enhanced location distinctiveness; (2) a multi-stage cascaded refinement process progressing from coarse fingerprinting initialization through to iterative geometric optimization; (3) an adaptive fusion mechanism that dynamically adjusts algorithm weights based on real-time channel quality assessment including signal-to-noise ratio (SNR) and geometric dilution of precision (GDOP); (4) a robust iteratively reweighted least squares (IRLS) solver with Huber M-estimation for outlier mitigation; and (5) Bayesian regularization incorporating fingerprinting estimates as informative priors. Comprehensive Monte Carlo simulations at 3.5 GHz carrier frequency with 400 MHz bandwidth demonstrate that AMSHL achieves a median localization error of 0.661 m, root-mean-squared error (RMSE) of 1.54 m, and mean-squared error (MSE) of 2.38 m2, with 87.5% probability of sub-2m accuracy, representing a 4.9× improvement over conventional hybrid fingerprinting in median error and a 7.1× reduction in MSE (from 16.83 m2 to 2.38 m2). An optional sigmoid-based fusion variant (AMSHL-S) further improves sub-2m accuracy to 89.4% by eliminating discrete switching artifacts. Furthermore, we provide theoretical analysis including Cramér–Rao lower bound (CRLB) derivation with an empirical MSE comparison to quantify the gap between practical algorithm performance and theoretical bounds (MSE-to-CRLB ratio of approximately 4.0×104), as well as a computational complexity assessment. All reported metrics have been cross-validated for internal consistency across formulas, tables, and textual descriptions; improvement factors and error statistics are verified against primary simulation outputs to ensure reproducibility. The complete simulation framework is made publicly available to facilitate reproducible research in RIS-aided positioning systems. Full article
(This article belongs to the Special Issue Indoor Localization Techniques Based on Wireless Communication)
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19 pages, 3887 KB  
Article
RELoc: An Enhanced 3D WiFi Fingerprinting Indoor Localization Algorithm with RFECV Feature Selection
by Shehu Lukman Ayinla, Azrina Abd Aziz, Micheal Drieberg, Misfa Susanto and Anis Laouiti
Sensors 2026, 26(1), 326; https://doi.org/10.3390/s26010326 - 4 Jan 2026
Cited by 1 | Viewed by 938
Abstract
The use of Artificial Intelligence (AI) algorithms has enhanced WiFi fingerprinting-based indoor localization. However, most existing approaches are limited to 2D coordinate estimation, which leads to significant performance declines in multi-floor environments due to vertical ambiguity and inadequate spatial modeling. This limitation reduces [...] Read more.
The use of Artificial Intelligence (AI) algorithms has enhanced WiFi fingerprinting-based indoor localization. However, most existing approaches are limited to 2D coordinate estimation, which leads to significant performance declines in multi-floor environments due to vertical ambiguity and inadequate spatial modeling. This limitation reduces reliability in real-world applications where accurate indoor localization is essential. This study proposes RELoc, a new 3D indoor localization framework that integrates Recursive Feature Elimination with Cross-Validation (RFECV) for optimal Access Point (AP) selection and Extremely Randomized Trees (ERT) for precise 2D and 3D coordinate regression. The ERT hyperparameters are optimized using Bayesian optimization with Optuna’s Tree-structured Parzen Estimator (TPE) to ensure robust, stable, and accurate localization. Extensive evaluation on the SODIndoorLoc and UTSIndoorLoc datasets demonstrates that RELoc delivers superior performance in both 2D and 3D indoor localization. Specifically, RELoc achieves Mean Absolute Errors (MAEs) of 1.84 m and 4.39 m for 2D coordinate prediction on SODIndoorLoc and UTSIndoorLoc, respectively. When floor information is incorporated, RELoc improves by 33.15% and 26.88% over the 2D version on these datasets. Furthermore, RELoc outperforms state-of-the-art methods by 7.52% over Graph Neural Network (GNN) and 12.77% over Deep Neural Network (DNN) on SODIndoorLoc and 40.22% over Extra Tree (ET) on UTSIndoorLoc, showing consistent improvements across various indoor environments. This enhancement emphasizes the critical role of 3D modeling in achieving robust and spatially discriminative indoor localization. Full article
(This article belongs to the Special Issue Indoor Localization Techniques Based on Wireless Communication)
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