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Leveraging Machine Learning for Enhanced Indoor Positioning Accuracy and Reliability
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Leveraging Machine Learning for Enhanced Indoor Positioning Accuracy and Reliability

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

Deadline for manuscript submissions: 15 June 2025 | Viewed by 2639

Special Issue Editors

Dr. Wenda Li

E-Mail Website
Guest Editor
School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK
Interests: passive radar; activity recognition; remote sensing; ISAC; digital health
Special Issues, Collections and Topics in MDPI journals
Dr. Yue Tian

E-Mail Website
Guest Editor
Fujian Key Laboratory of Communication Network and Information Processing, School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
Interests: integrated sensing; computation and communication; wireless sensor networks; mobile edge computing
Special Issues, Collections and Topics in MDPI journals
Dr. Shelly Vishwakarma

E-Mail Website
Guest Editor
Department of Electronics and Computer Science, University of Southampton, University Rd., Southampton SO17 1BJ, UK
Interests: active radar; passive radar; through-the-wall sensing; activity recognition; remote sensing; signal processing

Special Issue Information

Dear Colleagues,

Indoor localization has gained significant importance for various applications, including navigation, asset tracking, and location-based services in complex environments such as shopping malls, airports, hospitals, and industrial settings. Unlike GPS signals, indoor localization faces challenges like signal interference, multipath propagation, and environmental complexities that can degrade accuracy. Machine learning (ML) techniques, with their ability to model complex patterns and adapt to changing environments, offer promising solutions to enhance the performance of indoor positioning systems. This Special Issue aims to gather the latest advances in research and developments at the intersection of machine learning and indoor localization. Topics of interest for this Special Issue include, but are not limited to, the following: 

  • Machine learning for indoor positioning;
  • Explainable AI (XAI) in indoor positioning systems;
  • Real-time applications;
  • Prototype development;
  • Sensor fusion;
  • Device-based/passive systems;
  • Collaborative positioning with ML;
  • Energy-efficient ML algorithms for indoor positioning;
  • Hybrid models combining ML with traditional methods;
  • Simulation and synthetic data generation;
  • Case studies and real-world implementations.

Dr. Wenda Li
Dr. Yue Tian
Dr. Shelly Vishwakarma
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 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 positioning
  • machine learning
  • sensor fusion
  • explainable AI (XAI)

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Published Papers (2 papers)

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Research

13 pages, 3463 KiB  
Article
Data-Efficient Training of Gaussian Process Regression Models for Indoor Visible Light Positioning
by Jie Wu, Rui Xu, Runhui Huang and Xuezhi Hong
Sensors 2024, 24(24), 8027; https://doi.org/10.3390/s24248027 - 16 Dec 2024
Viewed by 701
Abstract
A data-efficient training method, namely Q-AL-GPR, is proposed for visible light positioning (VLP) systems with Gaussian process regression (GPR). The proposed method employs the methodology of active learning (AL) to progressively update the effective training dataset with data of low similarity to the [...] Read more.
A data-efficient training method, namely Q-AL-GPR, is proposed for visible light positioning (VLP) systems with Gaussian process regression (GPR). The proposed method employs the methodology of active learning (AL) to progressively update the effective training dataset with data of low similarity to the existing one. A detailed explanation of the principle of the proposed methods is given. The experimental study is carried out in a three-dimensional GPR-VLP system. The results show the superiority of the proposed method over both the conventional training method based on random draw and a previously proposed line-based AL training method. The impacts of the parameter of active learning on the performance of the GPR-VLP are also presented via experimental investigation, which shows that (1) the proposed training method outperforms the conventional one regardless of the number of final effective training data (E), especially for a small/moderate effective training dataset, (2) a moderate step size (k) should be chosen for updating the effective training dataset to balance the positioning accuracy and computational complexity, and (3) due to the interplay of the reliability of the initialized GPR model and the flexibility in reshaping such a model via active learning, the number of initial effective training data (m) should be optimized. In terms of data efficiency in training, the required number of training data can be reduced by ~27.8% by Q-AL-GPR for a mean positioning accuracy of 3 cm when compared with GPR. The CDF analysis shows that with the proposed training method, the 97th percentile positioning error of GPR-VLP with 300 training data is reduced from 11.8 cm to 7.5 cm, which corresponds to a ~36.4% improvement in positioning accuracy. Full article
(This article belongs to the Special Issue Leveraging Machine Learning for Enhanced Indoor Positioning Accuracy and Reliability)
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18 pages, 8730 KiB  
Article
A Novel Non-Contact Multi-User Online Indoor Positioning Strategy Based on Channel State Information
by Yixin Zhuang, Yue Tian and Wenda Li
Sensors 2024, 24(21), 6896; https://doi.org/10.3390/s24216896 - 27 Oct 2024
Viewed by 1434
Abstract
The IEEE 802.11bf-based wireless fidelity (WiFi) indoor positioning system has gained significant attention recently. It is important to recognize that multi-user online positioning occurs in real wireless environments. This paper proposes an indoor positioning sensing strategy that includes an optimized preprocessing process and [...] Read more.
The IEEE 802.11bf-based wireless fidelity (WiFi) indoor positioning system has gained significant attention recently. It is important to recognize that multi-user online positioning occurs in real wireless environments. This paper proposes an indoor positioning sensing strategy that includes an optimized preprocessing process and a new machine learning (ML) method called NKCK. The NKCK method can be broken down into three components: neighborhood component analysis (NCA) for dimensionality reduction, K-means clustering, and K-nearest neighbor (KNN) classification with cross-validation (CV). The KNN algorithm is particularly suitable for our dataset since it effectively classifies data based on proximity, relying on the spatial relationships between points. Experimental results indicate that the NKCK method outperforms traditional methods, achieving reductions in error rates of 82.4% compared to naive Bayes (NB), 85.0% compared to random forest (RF), 72.1% compared to support vector machine (SVM), 64.7% compared to multilayer perceptron (MLP), 50.0% compared to density-based spatial clustering of applications with noise (DBSCAN)-based methods, 42.0% compared to linear discriminant analysis (LDA)-based channel state information (CSI) amplitude fingerprinting, and 33.0% compared to principal component analysis (PCA)-based approaches. Due to the sensitivity of CSI, our multi-user online positioning system faces challenges in detecting dynamic human activities, such as human tracking, which requires further investigation in the future. Full article
(This article belongs to the Special Issue Leveraging Machine Learning for Enhanced Indoor Positioning Accuracy and Reliability)
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