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Indoor Navigation: Indoor Positioning System Using Sensing Technologies
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Indoor Navigation: Indoor Positioning System Using Sensing Technologies

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

Deadline for manuscript submissions: 25 December 2025 | Viewed by 2977

Special Issue Editor

Prof. Dr. Frank Deinzer

E-Mail Website
Guest Editor
Faculty of Computer Science and Business Information Systems, Technical University of Applied Sciences Wuerzburg-Schweinfurt, 97074 Wuerzburg, Germany
Interests: machine learning; pattern recognition; computer graphics; sensor fusion; indoor localization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Location-based services play a crucial role in our daily lives, aiding in navigation for vehicles and pedestrians. However, significant portions of our activities occur indoors—whether at home, work, shopping malls, museums, or during travels. Traditional outdoor navigation technologies, such as Global Navigation Satellite Systems, are ineffective indoors due to weak signal reception, posing a challenge for context-aware services vital for social networking, advertising, recommendation systems, and healthcare.

Indoor positioning systems address this challenge by leveraging a variety of sensing technologies, including Wi-Fi, Bluetooth, RFID, Ultra-Wide Band, and IMU sensors. The complexity of indoor environments, combined with human motion patterns and uncertain sensor data, necessitates modeling indoor positioning with powerful mathematical models (typically non-linear and non-Gaussian) to accurately predict pedestrian locations.

Sensor fusion plays a pivotal role by integrating data from multiple sensors, improving accuracy and reliability. This Special Issue explores innovative approaches in indoor navigation and positioning using advanced sensing technologies. Researchers are invited to contribute their cutting-edge findings, focusing on solutions that combine various sensing technologies to enhance the effectiveness of indoor positioning systems.

Prof. Dr. Frank Deinzer
Guest Editor

Toni Fetzer (toni.fetzer@cronn.de)
Guest Editor Assistant

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 100 words) can be sent to the Editorial Office for announcement on this website.

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 navigation
  • positioning systems
  • sensing technologies
  • sensor fusion
  • Wi-Fi/bluetooth
  • ultra-wideband
  • inertial measurement unit
  • non-linear systems

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Published Papers (3 papers)

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23 pages, 678 KiB  
Article
Unified Probabilistic and Similarity-Based Position Estimation from Radio Observations
by Max Werner, Markus Bullmann, Toni Fetzer and Frank Deinzer
Sensors 2025, 25(13), 4092; https://doi.org/10.3390/s25134092 - 30 Jun 2025
Viewed by 225
Abstract
We propose a modeling approach for position estimation based on the observed radio propagation in an environment. The approach is purely similarity-based and therefore free of explicit physical assumptions. What distinguishes it from classical related methods are probabilistic position estimates. Instead of just [...] Read more.
We propose a modeling approach for position estimation based on the observed radio propagation in an environment. The approach is purely similarity-based and therefore free of explicit physical assumptions. What distinguishes it from classical related methods are probabilistic position estimates. Instead of just providing a point estimate for a given signal sequence, our model returns the distribution of possible positions as continuous probability density function, which allows for appropriate integration into recursive state estimation systems. The estimation procedure starts by using a kernel to compare incoming data with reference recordings from known positions. Based on the obtained similarities, weights are assigned to the reference positions. An arbitrarily chosen density estimation method is then applied given this assignment. Thus, a continuous representation of the distribution of possible positions in the environment is provided. We apply the solution in a Particle Filter (PF) system for smartphone-based indoor localization. The approach is tested both with radio signal strength (RSS) measurements (Wi-Fi and Bluetooth Low Energy RSSI) and round-trip time (RTT) measurements, given by Wi-Fi Fine Timing Measurement. Compared to distance-based models, which are dedicated to the specific physical properties of each measurement type, our similarity-based model achieved overall higher accuracy at tracking pedestrians under realistic conditions. Since it does not explicitly consider the physics of radio propagation, the proposed model has also been shown to work flexibly with either RSS or RTT observations. Full article
(This article belongs to the Special Issue Indoor Navigation: Indoor Positioning System Using Sensing Technologies)
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16 pages, 468 KiB  
Article
Modeling and Analysis of Dispersive Propagation of Structural Waves for Vibro-Localization
by Murat Ambarkutuk and Paul E. Plassmann
Sensors 2024, 24(23), 7744; https://doi.org/10.3390/s24237744 - 4 Dec 2024
Viewed by 717
Abstract
The dispersion of structural waves, where wave speed varies with frequency, introduces significant challenges in accurately localizing occupants in a building based on vibrations caused by their movements. This study presents a novel multi-sensor vibro-localization technique that accounts for dispersion effects, enhancing the [...] Read more.
The dispersion of structural waves, where wave speed varies with frequency, introduces significant challenges in accurately localizing occupants in a building based on vibrations caused by their movements. This study presents a novel multi-sensor vibro-localization technique that accounts for dispersion effects, enhancing the accuracy and robustness of occupant localization. The proposed method utilizes a model-based approach to parameterize key propagation phenomena, including wave dispersion and attenuation, which are fitted to observed waveforms. The localization is achieved by maximizing the joint likelihood of the occupant’s location based on sensor measurements. The effectiveness of the proposed technique is validated using two experimental datasets: one from a controlled environment involving an aluminum plate and the other from a building-scale experiment conducted at Goodwin Hall, Virginia Tech. Results for the proposed algorithm demonstrates a significant improvement in localization accuracy compared to benchmark algorithms. Specifically, in the aluminum plate experiments, the proposed technique reduced the average localization precision from 7.77 cm to 1.97 cm, representing a ∼74% improvement. Similarly, in the Goodwin Hall experiments, the average localization error decreased from 0.67 m to 0.3 m, with a ∼55% enhancement in accuracy. These findings indicate that the proposed approach outperforms existing methods in accurately determining occupant locations, even in the presence of dispersive wave propagation. Full article
(This article belongs to the Special Issue Indoor Navigation: Indoor Positioning System Using Sensing Technologies)
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19 pages, 2077 KiB  
Article
Application of Indoor Positioning Systems in Nursing Homes: Enhancing Resident Safety and Staff Efficiency
by Chia-Rong Lee, Edward T.-H. Chu, Min-Jing Sie, Li-Tsai Lin, Mei-Zhen Hong and Ching-Chih Huang
Sensors 2024, 24(18), 6099; https://doi.org/10.3390/s24186099 - 20 Sep 2024
Cited by 2 | Viewed by 1498
Abstract
Providing a safe and secure living environment for residents that is supported by a dedicated healthcare team is one of the core values of nursing homes. Nursing homes must protect residents from the risk of going missing, track quarantined residents and visitors to [...] Read more.
Providing a safe and secure living environment for residents that is supported by a dedicated healthcare team is one of the core values of nursing homes. Nursing homes must protect residents from the risk of going missing, track quarantined residents and visitors to control the spread of infection, and maintain proactive nursing rounds. However, recruiting and retaining qualified caregivers and medical staff has long been a challenge. Therefore, using advanced technology to ensure the safety and security of residents is highly desirable. In this work, we first demonstrate the applicability of indoor tracking applications in a nursing home, such as resident and asset tracking, nursing assistant management, visitor tracking, infection control, and vital-sign monitoring. To monitor the locations of residents and staff, Bluetooth tags were used, providing real-time data for location tracking. We then conduct a series of quantitative analyses to illustrate how indoor tracking data can support the management of nursing homes, including characterizing residents’ activities in daily living and assessing the performance and workload of nursing assistants. Finally, we use qualitative research to evaluate the acceptability of an indoor positioning system in the nursing home. The results show that the implemented indoor positioning applications can improve the quality of healthcare and working efficiency, thereby providing a safer and more secure living environment for residents. Full article
(This article belongs to the Special Issue Indoor Navigation: Indoor Positioning System Using Sensing Technologies)
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