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Sensors for Indoor Positioning

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

Deadline for manuscript submissions: closed (20 March 2026) | Viewed by 989

Special Issue Editors


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Guest Editor
Department of Electronics, Polytechnic School Office O-334, University of Alcalá, Campus Universitario, Alcalá, 28871 Madrid, Spain
Interests: intelligent sensors; indoor positioning systems; visible light positioning; optical sensorial systems; sensor networks; electronic design
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electronics, University of Alcalá, University Campus, Madrid-Barcelona Road, km 33, Alcalá de Henares, 28805 Madrid, Spain
Interests: intelligent sensors; optical sensors; location; sensor networks; visible light positioning (VLP); visible light communication (VLC); indoor localization; indoor positioning system (IPS); position-sensitive detectors (PSD); multipath; vehicle-to-vehicle (V2V); vehicle-to-infrastructure (V2I)
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electronics, University of Alcala, 28871 Madrid, Spain
Interests: smart sensors; FPGAs; embedded design; WSN; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The growing demand for location-based services in indoor environments has significantly fostered research and development in high-precision positioning technologies. Unlike outdoor scenarios, indoor localization requires specialized sensors and algorithms capable of operating robustly against signal attenuation, multipath propagation, and architectural heterogeneity. In this context, sensors and strategies for indoor positioning constitute an interdisciplinary framework that encompasses optical, radio-frequency, ultrasonic, and inertial technologies, as well as hybrid approaches that integrate multiple sources of information.

This Special Issue aims to gather contributions addressing recent advances in sensors and indoor localization techniques. Submissions are expected to include both hardware innovations, such as novel signal processors, smart antennas, optical, RF, ultrasonic, or inertial sensors, and wearable devices, as well as developments in algorithms for data fusion, machine learning, and trajectory estimation. Equally relevant are works focused on practical applications in areas such as service robotics, warehouse logistics, healthcare assistance, augmented reality, and intelligent building management. By bringing together these perspectives, the journal seeks to provide both an updated overview and a forward-looking vision of this rapidly evolving field.

Prof. Dr. José Luis Lázaro-Galilea
Dr. Álvaro de la Llana Calvo
Prof. Dr. Alfredo Gardel
Prof. Dr. Ignacio Bravo
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
  • localization sensors
  • data fusion
  • machine learning
  • inertial navigation
  • radio-frequency localization
  • optical and ultrasonic sensing

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Published Papers (1 paper)

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Research

20 pages, 3982 KB  
Article
AI-Driven Decimeter-Level Indoor Localization Using Single-Link Wi-Fi: Adaptive Clustering and Probabilistic Multipath Mitigation
by Li-Ping Tian, Chih-Min Yu, Li-Chun Wang and Zhizhang (David) Chen
Sensors 2026, 26(2), 642; https://doi.org/10.3390/s26020642 - 18 Jan 2026
Viewed by 674
Abstract
This paper presents an Artificial Intelligence (AI)-driven framework for high-precision indoor localization using single-link Wi-Fi channel state information (CSI), targeting real-time deployment in complex multipath environments. To overcome challenges such as signal distortion and environmental dynamics, the proposed system integrates adaptive and unsupervised [...] Read more.
This paper presents an Artificial Intelligence (AI)-driven framework for high-precision indoor localization using single-link Wi-Fi channel state information (CSI), targeting real-time deployment in complex multipath environments. To overcome challenges such as signal distortion and environmental dynamics, the proposed system integrates adaptive and unsupervised intelligence modules into the localization pipeline. A refined two-stage time-of-flight (TOF) estimation method is introduced, combining a minimum-norm algorithm with a probability-weighted refinement mechanism that improves ranging accuracy under non-line-of-sight (NLOS) conditions. Simultaneously, an adaptive parameter-tuned DBSCAN algorithm is applied to angle-of-arrival (AOA) sequences, enabling unsupervised spatio-temporal clustering for stable direction estimation without requiring prior labels or environmental calibration. These AI-enabled components allow the system to dynamically suppress multipath interference, eliminate positioning ambiguity, and maintain robustness across diverse indoor layouts. Comprehensive experiments conducted on the Widar2.0 dataset demonstrate that the proposed method achieves decimeter-level accuracy with an average localization error of 0.63 m, outperforming existing methods such as “Widar2.0” and “Dynamic-MUSIC” in both accuracy and efficiency. This intelligent and lightweight architecture is fully compatible with commodity Wi-Fi hardware and offers significant potential for real-time human tracking, smart building navigation, and other location-aware AI applications. Full article
(This article belongs to the Special Issue Sensors for Indoor Positioning)
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