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Development and Challenges of Indoor Positioning and Localization
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Development and Challenges of Indoor Positioning and Localization

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 6259

Special Issue Editors

Dr. María Del Carmen Pérez-Rubio

E-Mail Website
Guest Editor
Electronics Department, Polytechnical School, University of Alcala, Alcalá de Henares, 28805 Madrid, Spain
Interests: ultrasonic indoor positioning systems; sequence design; ambient intelligence for independent living
Special Issues, Collections and Topics in MDPI journals
Dr. Elena Aparicio Esteve

E-Mail Website
Guest Editor
Electronics Department, Polytechnical School, University of Alcala, Alcalá de Henares, 28805 Madrid, Spain
Interests: optical indoor positioning systems; localization algorithms; machine learning techniques; sensor fusion
Prof. Dr. Carlos De Marziani

E-Mail Website
Guest Editor
Instituto Multidisciplinario para la Investigación y el Desarrollo Productivo y Social de la Cuenca Golfo San Jorge, Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de la Patagonia San Juan Bosco, Comodoro Rivadavia, Argentina
Interests: indoor positioning systems; underwater positioning systems; sequence design; localization algorithms

Special Issue Information

Dear Colleagues,

Indoor positioning and localization have become essential for various applications, including smart buildings, robotics, healthcare, or logistics. Unlike outdoor positioning systems such as GPS, indoor environments present unique challenges due to multipath interference, signal attenuation, and complex spatial constraints. This Special Issue will explore the latest advancements in indoor positioning technologies, including signal-based methods (Wi-Fi, Bluetooth, UWB, RFID, acoustic, optical, etc.), vision-based approaches, sensor fusion, and machine learning techniques. Additionally, we seek to address key challenges such as accuracy, scalability, energy efficiency, pervasive and non-invasive methods and privacy concerns. Contributions may include theoretical developments, experimental results, novel algorithms, and real-world applications. We invite researchers and industry experts to submit original research papers, reviews, and case studies that advance the field of indoor positioning and localization.

Dr. María Del Carmen Pérez-Rubio
Dr. Elena Aparicio Esteve
Prof. Dr. Carlos De Marziani
Guest Editors

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

  • indoor positioning systems (IPSs)
  • localization algorithms
  • sensor fusion
  • Wi-Fi, Bluetooth, UWB, and RFID-based positioning
  • vision-based localization, infrared, magnetic, and ultrasound
  • machine learning for indoor localization
  • accuracy and error mitigation
  • real-world applications

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Published Papers (4 papers)

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31 pages, 5065 KB  
Article
AdaFed-LDR: Adaptive Federated Learning with Layerwise Dynamics Regularization for Robust Wi-Fi Localization
by Kaito Harada, Hirofumi Natori, Makoto Koike and Hiroshi Mineno
Sensors 2026, 26(10), 3148; https://doi.org/10.3390/s26103148 - 15 May 2026
Viewed by 304
Abstract
Wi-Fi Channel State Information (CSI)-based indoor localization enables high-precision positioning, but its deployment across multiple environments faces two major challenges: privacy concerns from centralizing CSI data, and severe statistical heterogeneity (non-IID) arising from the strong environment-dependency of CSI. This heterogeneity creates a stability–plasticity [...] Read more.
Wi-Fi Channel State Information (CSI)-based indoor localization enables high-precision positioning, but its deployment across multiple environments faces two major challenges: privacy concerns from centralizing CSI data, and severe statistical heterogeneity (non-IID) arising from the strong environment-dependency of CSI. This heterogeneity creates a stability–plasticity trade-off in federated learning—maintaining precision in known environments (stability) while adapting to unseen domains (plasticity). To address this trade-off, we propose AdaFed-LDR, which combines server-side Confidence-Weighted Adaptive Aggregation with client-side Layerwise Dynamics Regularization (LDR). The aggregation recalibrates client contributions based on feature covariance changes, while LDR imposes depth-dependent constraints—stronger constraints on shallow layers to preserve environment-agnostic features and weaker constraints on deeper layers to allow environment-specific adaptation. Evaluated across 8 indoor environments using Leave-One-Out Cross-Validation and 5 random seeds, AdaFed-LDR achieved a mean localization error (MLE) of 0.41 cm in known environments, corresponding to an 88.2% reduction compared with FedAvg. In domain generalization to unseen environments, AdaFed-LDR achieved an MLE of 218.2±2.8 cm, demonstrating an improvement over FedPos (257.6±14.04 cm). With one adaptation sample per reference point, MLE improved to 21 cm. Ablation experiments confirmed that combining the two proposed components achieved the highest improvement (83.9%) compared with applying them individually, supporting AdaFed-LDR as a reproducible approach to the stability–plasticity trade-off in federated CSI-based localization. Full article
(This article belongs to the Special Issue Development and Challenges of Indoor Positioning and Localization)
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28 pages, 14788 KB  
Article
A Practical Case of Monitoring Older Adults Using mmWave Radar and UWB
by Gabriel García-Gutiérrez, Elena Aparicio-Esteve, Jesús Ureña, José Manuel Villadangos-Carrizo, Ana Jiménez-Martín and Juan Jesús García-Domínguez
Sensors 2026, 26(2), 681; https://doi.org/10.3390/s26020681 - 20 Jan 2026
Viewed by 1334
Abstract
Population aging is driving the need for unobtrusive, continuous monitoring solutions in residential care environments. Radio-frequency (RF)-based technologies such as Ultra-Wideband (UWB) and millimeter-wave (mmWave) radar are particularly attractive for providing detailed information on presence and movement while preserving privacy. Building on a [...] Read more.
Population aging is driving the need for unobtrusive, continuous monitoring solutions in residential care environments. Radio-frequency (RF)-based technologies such as Ultra-Wideband (UWB) and millimeter-wave (mmWave) radar are particularly attractive for providing detailed information on presence and movement while preserving privacy. Building on a UWB–mmWave localization system deployed in a senior living residence, this paper focuses on the data-processing methodology for extracting quantitative mobility indicators from long-term indoor monitoring data. The system combines a device-free mmWave radar setup in bedrooms and bathrooms with a tag-based UWB positioning system in common areas. For mmWave data, an adaptive short-term average/long-term average (STA/LTA) detector operating on an aggregated, normalized radar energy signal is used to classify micro- and macromovements into bedroom occupancy and non-sedentary activity episodes. For UWB data, a partially constrained Kalman filter with a nearly constant velocity dynamics model and floor-plan information yields smoothed trajectories, from which daily gait- and mobility-related metrics are derived. The approach is illustrated using one-day samples from three users as a proof of concept. The proposed methodology provides individualized indicators of bedroom occupancy, sedentary behavior, and mobility in shared spaces, supporting the feasibility of combined UWB and mmWave radar sensing for longitudinal routine analysis in real-world elderly care environments. Full article
(This article belongs to the Special Issue Development and Challenges of Indoor Positioning and Localization)
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24 pages, 60464 KB  
Article
Novel Filter-Based Excitation Method for Pulse Compression in Ultrasonic Sensory Systems
by Álvaro Cortés, María Carmen Pérez-Rubio and Álvaro Hernández
Sensors 2026, 26(1), 99; https://doi.org/10.3390/s26010099 - 23 Dec 2025
Viewed by 696
Abstract
Location-based services (LBSs) and positioning systems have spread worldwide due to the emergence of Internet of Things (IoT) and other application domains that require real-time estimation of the position of a person, tag, or asset in general in order to provide users with [...] Read more.
Location-based services (LBSs) and positioning systems have spread worldwide due to the emergence of Internet of Things (IoT) and other application domains that require real-time estimation of the position of a person, tag, or asset in general in order to provide users with services and apps with added value. Whereas Global Navigation Satellite Systems (GNSSs) are well-established solutions outdoors, positioning is still an open challenge indoors, where different sensory technologies may be considered for that purpose, such as radio frequency, infrared, or ultrasounds, among others. With regard to ultrasonic systems, previous works have already developed indoor positioning systems capable of achieving accuracies in the range of centimeters but limited to a few square meters of coverage and severely affected by the Doppler effect coming from moving targets, which significantly degrades the overall positioning performance. Furthermore, the actual bandwidth available in commercial transducers often constrains the ultrasonic transmission, thus reducing the position accuracy as well. In this context, this work proposes a novel excitation and processing method for an ultrasonic positioning system, which significantly improves the transmission capabilities between an emitter and a receiver. The proposal employs a superheterodyne approach, enabling simultaneous transmission and reception of signals across multiple channels. It also adapts the bandwidths and central frequencies of the transmitted signals to the specific bandwidth characteristics of available transducers, thus optimizing the system performance. Binary spread spectrum sequences are utilized within a multicarrier modulation framework to ensure robust signal transmission. The ultrasonic signals received are then processed using filter banks and matched filtering techniques to determine the Time Differences of Arrival (TDoA) for every transmission, which are subsequently used to estimate the target position. The proposal has been modeled and successfully validated using a digital twin. Furthermore, experimental tests on the prototype have also been conducted to evaluate the system’s performance in real scenarios, comparing it against classical approaches in terms of ranging distance, signal-to-noise ratio (SNR), or multipath effects. Experimental validation demonstrates that the proposed narrowband scheme reliably operates at distances up to 40 m, compared to the 34 m limit of conventional wideband approaches. Ranging errors remain below 3 cm at 40 m, whereas the wideband scheme exhibits errors exceeding 8 cm. Furthermore, simulation results show that the narrowband scheme maintains stable operation at SNR as low as −32 dB, whereas the wideband one only achieves up to −17 dB, highlighting the significant performance advantages of the proposed approach in both experimental and simulated scenarios. Full article
(This article belongs to the Special Issue Development and Challenges of Indoor Positioning and Localization)
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49 pages, 1236 KB  
Systematic Review
From Fingerprinting to Advanced Machine Learning: A Systematic Review of Wi-Fi and BLE-Based Indoor Positioning Systems
by Sara Martín-Frechina, Esther Dura, Ignacio Miralles and Joaquín Torres-Sospedra
Sensors 2025, 25(22), 6946; https://doi.org/10.3390/s25226946 - 13 Nov 2025
Cited by 7 | Viewed by 3059
Abstract
The Indoor Positioning System (IPS) is used to locate devices and people in smart environments. In recent years, position determination methods have evolved from simple Received Signal Strength Indicator (RSSI) measurements to more advanced approaches such as Channel State Information (CSI), Round Trip [...] Read more.
The Indoor Positioning System (IPS) is used to locate devices and people in smart environments. In recent years, position determination methods have evolved from simple Received Signal Strength Indicator (RSSI) measurements to more advanced approaches such as Channel State Information (CSI), Round Trip Time (RTT), and Angle of Arrival (AoA), increasingly combined with Machine Learning (ML). This article presents a systematic review of the literature on ML-based IPS using IEEE 802.11 Wireless LAN (Wi-Fi) and Bluetooth Low Energy (BLE), including studies published between 2020 and 2024 under the Preferred Reporting Items for Systematic Reviews and Meta-Analyse (PRISMA) methodology. This study examines the techniques used to collect measurements and the ML models used, and discusses the growing use of Deep Learning (DL) approaches. This review identifies some challenges that remain for the implementation of these systems, such as environmental variability, device heterogeneity, and the need for calibration. Future research should expand ML applications to RTT and AoA, explore hybrid multimetric systems, and design lightweight, adaptive DL models. Advances in wireless standards and emerging technologies are also expected to further enhance accuracy and scalability in next-generation IPS. Full article
(This article belongs to the Special Issue Development and Challenges of Indoor Positioning and Localization)
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