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Innovative Optical Sensors for Navigation and Positioning Systems
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Innovative Optical Sensors for Navigation and Positioning Systems

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

Deadline for manuscript submissions: 25 March 2026 | Viewed by 495

Special Issue Editor

Dr. Álvaro de la Llana Calvo

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 mitigation; vehicle-to-vehicle (V2V); vehicle-to-infrastructure (V2I)
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Special Issue Information

Dear Colleagues,

Optical sensing technologies are playing an increasingly central role in the development of advanced navigation and positioning systems. Their inherent advantages—such as high spatial resolution, immunity to electromagnetic interference, and compatibility with existing lighting infrastructure—make them ideal for a wide range of applications, ranging from indoor localization to vehicle-to-vehicle communication.

This Special Issue aims to gather original research and review articles on innovative optical sensors and their integration into navigation and positioning systems. Topics of interest include, but are not limited to, the following: visible light positioning (VLP); infrared positioning; optical angle-of-arrival (AoA) and time-of-flight (ToF) techniques; position-sensitive detectors (PSD); quadrant angular diversity aperture photodiode (QADA); and hybrid systems combining optical sensing with inertial or radio-based technologies.

The scope of this Special Issue also includes optical communication for navigation purposes, such as visible light communication (VLC) in vehicular or infrastructure-based systems, where positioning and data exchange converge.

Dr. Álvaro de la Llana Calvo
Guest Editor

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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

  • optical sensors
  • visible light positioning (VLP)
  • visible light communication (VLC)
  • indoor localization
  • indoor positioning system (IPS)
  • vehicle-to-vehicle (V2V)
  • vehicle-to-infrastructure (V2I)
  • navigation systems
  • position-sensitive detectors (PSD)
  • quadrant angular diversity aperture photodiode (QADA)
  • sensor fusion
  • multipath mitigation
  • smart mobility

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Published Papers (1 paper)

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Research

17 pages, 1943 KB  
Article
Improving Visible Light Positioning Accuracy Using Particle Swarm Optimization (PSO) for Deep Learning Hyperparameter Updating in Received Signal Strength (RSS)-Based Convolutional Neural Network (CNN)
by Chun-Ming Chang, Yuan-Zeng Lin and Chi-Wai Chow
Sensors 2025, 25(23), 7256; https://doi.org/10.3390/s25237256 - 28 Nov 2025
Viewed by 338
Abstract
Visible light positioning (VLP) has emerged as a promising indoor positioning technology, owing to its high accuracy and cost-effectiveness. In practical scenarios, signal attenuation, multiple light reflections, or light-deficient regions, particularly near room corners or furniture, can significantly degrade the light quality. In [...] Read more.
Visible light positioning (VLP) has emerged as a promising indoor positioning technology, owing to its high accuracy and cost-effectiveness. In practical scenarios, signal attenuation, multiple light reflections, or light-deficient regions, particularly near room corners or furniture, can significantly degrade the light quality. In addition, the non-uniform light distribution by light-emitting diode (LED) luminaires can also introduce errors in VLP estimation. To mitigate these challenges, recent studies have increasingly explored the use of machine learning (ML) techniques to model the complex nonlinear characteristics of indoor optical channels and improve VLP performance. Convolutional neural networks (CNNs) have demonstrated strong potential in reducing positioning errors and improving system robustness under non-ideal lighting conditions. However, the performance of CNN-based systems is highly sensitive to their hyperparameters, including learning rate, dropout rate, batch size, and optimizer selection. Manual tuning of these parameters is not only time-consuming but also often suboptimal, particularly when models are applied to new or dynamic environments. Therefore, there is a growing need for automated optimization techniques that can adaptively determine optimal model configurations for VLP tasks. In this work, we propose and demonstrate a VLP system that integrates received signal strength (RSS) signal pre-processing, a CNN, and particle swarm optimization (PSO) for automated hyperparameter tuning. In the proof-of-concept VLP experiment, three different height layer planes (i.e., 200, 225, and 250 cm) are employed for the comparison of three different ML models, including linear regression (LR), an artificial neural network (ANN), and a CNN. For instance, the mean positioning error of a CNN + pre-processing model at the 200 cm receiver (Rx)-plane reduces from 9.83 cm to 5.72 cm. This represents an improvement of 41.81%. By employing a CNN + pre-processing + PSO, the mean error can be further reduced to 4.93 cm. These findings demonstrate that integrating PSO-based hyperparameter tuning with a CNN and RSS pre-processing significantly enhances positioning accuracy, reliability, and model robustness. This approach offers a scalable and effective solution for real-world indoor positioning applications in smart buildings and Internet of Things (IoT) environments. Full article
(This article belongs to the Special Issue Innovative Optical Sensors for Navigation and Positioning Systems)
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