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Advancements in Wireless Localization: Enhancing Object Detection, Navigation and Communications
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Advancements in Wireless Localization: Enhancing Object Detection, Navigation and Communications

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

Deadline for manuscript submissions: 25 August 2025 | Viewed by 7394

Special Issue Editors

Dr. Ahmad Alsharoa

E-Mail Website
Guest Editor
Electrical and Computer Engineering Department, Missouri University of Science and Technology, Rolla, MO, USA
Interests: LiDAR; wireless localization; sensors; gesture identification; unmanned aircraft system; deep learning; object detection and tracking; UAV; 3D light detection and ranging
Dr. Abdelaali Chaoub

E-Mail Website
Guest Editor
Institut National des Postes et Télécommunications (INPT), Rabat, Morocco
Interests: architectural contributions to autonomous and self-organizing systems; design and optimization of IoT-enabled smart grids; remote and rural connectivity solutions for B5G/6G networks; dynamic spectrum access techniques for cognitive radio networks
Dr. Mohamed Selim

E-Mail Website
Guest Editor
Electrical and Computer Engineering Department, Iowa State University, Ames, IA 50011, USA
Interests: self-organizing networks; drone-based communications; optimization for cellular networks; integrated sensing and communications (ISC); reconfigurable intelligent surfaces (RIS)

Special Issue Information

Dear Colleagues,

Wireless localization technology has emerged as pivotal tools in various applications, particularly in object detection and localization. Utilizing advanced algorithms and signal processing techniques, wireless localization systems determine the location of devices or objects within a wireless network. This Special Issue aims to explore the advancements, challenges, and applications of wireless localization technology in the realms of object detection and wireless localization. It is also delving into its role in enhancing wireless communications as well as the potential of breakthrough technologies such as joint sensing and communications to improve localization accuracy.

Potential topics include but are not limited to the following:

  1. Wireless localization.
  2. Real-time localization.
  3. Object and activity detection; activity classifications and recognition.
  4. LiDAR-based localization.
  5. Radar-based localization.
  6. Muti-sensor integration for localization.
  7. Mobile device tracking.
  8. Positioning systems (indoor and outdoor positioning).
  9. Localization algorithms.
  10. Joint sensing and communications.

Dr. Ahmad Alsharoa
Dr. Abdelaali Chaoub
Dr. Mohamed Selim
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

  • wireless localization
  • real-time localization
  • object and activity detection
  • activity classifications and recognition
  • LiDAR-based localization
  • radar-based localization
  • muti-sensor integration for localization
  • mobile device tracking
  • positioning systems (indoor and outdoor positioning)
  • localization algorithms
  • joint sensing and communications

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Published Papers (5 papers)

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Research

21 pages, 6770 KiB  
Article
Revolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS Beamforming
by Ahmad M. Nazar, Mohamed Y. Selim and Daji Qiao
Sensors 2025, 25(1), 75; https://doi.org/10.3390/s25010075 (registering DOI) - 26 Dec 2024
Abstract
Reconfigurable Intelligent Surface (RIS) panels have garnered significant attention with the emergence of next-generation network technologies. This paper proposes a novel data-driven approach that leverages Light Detecting and Ranging (LiDAR) sensors to enhance user localization and beamforming in RIS-assisted networks. Integrating LiDAR sensors [...] Read more.
Reconfigurable Intelligent Surface (RIS) panels have garnered significant attention with the emergence of next-generation network technologies. This paper proposes a novel data-driven approach that leverages Light Detecting and Ranging (LiDAR) sensors to enhance user localization and beamforming in RIS-assisted networks. Integrating LiDAR sensors into the network will be instrumental, offering high-speed and precise 3D mapping capabilities, even in low light or adverse weather conditions. LiDAR data facilitate user localization, enabling the determination of optimal RIS coefficients. Our approach extends a Graph Neural Network (GNN) by integrating LiDAR-captured user locations as inputs. This extension enables the GNN to effectively learn the mapping from received pilots to optimal beamformers and reflection coefficients to maximize the RIS-assisted sumrate among multiple users. The permutation-equivariant and -invariant properties of the GNN proved advantageous in efficiently handling the LiDAR data. Our simulation results demonstrated significant improvements in sum rates compared with conventional methods. Specifically, including locations improved on excluding locations by up to 25% and outperformed the Linear Minimum Mean Squared Error (LMMSE) channel estimation by up to 85% with varying downlink power and 98% with varying pilot lengths, and showed a remarkable 190% increase with varying downlink power compared with scenarios excluding the RIS. Full article
(This article belongs to the Special Issue Advancements in Wireless Localization: Enhancing Object Detection, Navigation and Communications)
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17 pages, 812 KiB  
Article
Enhancing Direction-of-Arrival Estimation with Multi-Task Learning
by Simone Bianco, Luigi Celona, Paolo Crotti, Paolo Napoletano, Giovanni Petraglia and Pietro Vinetti
Sensors 2024, 24(22), 7390; https://doi.org/10.3390/s24227390 - 20 Nov 2024
Viewed by 638
Abstract
There are numerous methods in the literature for Direction-of-Arrival (DOA) estimation, including both classical and machine learning-based approaches that jointly estimate the Number of Sources (NOS) and DOA. However, most of these methods do not fully leverage the potential synergies between these two [...] Read more.
There are numerous methods in the literature for Direction-of-Arrival (DOA) estimation, including both classical and machine learning-based approaches that jointly estimate the Number of Sources (NOS) and DOA. However, most of these methods do not fully leverage the potential synergies between these two tasks, which could yield valuable shared information. To address this limitation, in this article, we present a multi-task Convolutional Neural Network (CNN) capable of simultaneously estimating both the NOS and the DOA of the signal. Through experiments on simulated data, we demonstrate that our proposed model surpasses the performance of state-of-the-art methods, especially in challenging environments characterized by high noise levels and dynamic conditions. Full article
(This article belongs to the Special Issue Advancements in Wireless Localization: Enhancing Object Detection, Navigation and Communications)
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14 pages, 15950 KiB  
Article
Uncertainty-Aware Depth Network for Visual Inertial Odometry of Mobile Robots
by Jimin Song, HyungGi Jo, Yongsik Jin and Sang Jun Lee
Sensors 2024, 24(20), 6665; https://doi.org/10.3390/s24206665 - 16 Oct 2024
Cited by 1 | Viewed by 3032
Abstract
Simultaneous localization and mapping, a critical technology for enabling the autonomous driving of vehicles and mobile robots, increasingly incorporates multi-sensor configurations. Inertial measurement units (IMUs), known for their ability to measure acceleration and angular velocity, are widely utilized for motion estimation due to [...] Read more.
Simultaneous localization and mapping, a critical technology for enabling the autonomous driving of vehicles and mobile robots, increasingly incorporates multi-sensor configurations. Inertial measurement units (IMUs), known for their ability to measure acceleration and angular velocity, are widely utilized for motion estimation due to their cost efficiency. However, the inherent noise in IMU measurements necessitates the integration of additional sensors to facilitate spatial understanding for mapping. Visual–inertial odometry (VIO) is a prominent approach that combines cameras with IMUs, offering high spatial resolution while maintaining cost-effectiveness. In this paper, we introduce our uncertainty-aware depth network (UD-Net), which is designed to estimate both depth and uncertainty maps. We propose a novel loss function for the training of UD-Net, and unreliable depth values are filtered out to improve VIO performance based on the uncertainty maps. Experiments were conducted on the KITTI dataset and our custom dataset acquired from various driving scenarios. Experimental results demonstrated that the proposed VIO algorithm based on UD-Net outperforms previous methods with a significant margin. Full article
(This article belongs to the Special Issue Advancements in Wireless Localization: Enhancing Object Detection, Navigation and Communications)
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24 pages, 6889 KiB  
Article
SOD-YOLOv8—Enhancing YOLOv8 for Small Object Detection in Aerial Imagery and Traffic Scenes
by Boshra Khalili and Andrew W. Smyth
Sensors 2024, 24(19), 6209; https://doi.org/10.3390/s24196209 - 25 Sep 2024
Cited by 1 | Viewed by 2317
Abstract
Object detection, as a crucial aspect of computer vision, plays a vital role in traffic management, emergency response, autonomous vehicles, and smart cities. Despite the significant advancements in object detection, detecting small objects in images captured by high-altitude cameras remains challenging, due to [...] Read more.
Object detection, as a crucial aspect of computer vision, plays a vital role in traffic management, emergency response, autonomous vehicles, and smart cities. Despite the significant advancements in object detection, detecting small objects in images captured by high-altitude cameras remains challenging, due to factors such as object size, distance from the camera, varied shapes, and cluttered backgrounds. To address these challenges, we propose small object detection YOLOv8 (SOD-YOLOv8), a novel model specifically designed for scenarios involving numerous small objects. Inspired by efficient generalized feature pyramid networks (GFPNs), we enhance multi-path fusion within YOLOv8 to integrate features across different levels, preserving details from shallower layers and improving small object detection accuracy. Additionally, we introduce a fourth detection layer to effectively utilize high-resolution spatial information. The efficient multi-scale attention module (EMA) in the C2f-EMA module further enhances feature extraction by redistributing weights and prioritizing relevant features. We introduce powerful-IoU (PIoU) as a replacement for CIoU, focusing on moderate quality anchor boxes and adding a penalty based on differences between predicted and ground truth bounding box corners. This approach simplifies calculations, speeds up convergence, and enhances detection accuracy. SOD-YOLOv8 significantly improves small object detection, surpassing widely used models across various metrics, without substantially increasing the computational cost or latency compared to YOLOv8s. Specifically, it increased recall from 40.1% to 43.9%, precision from 51.2% to 53.9%, mAP0.5 from 40.6% to 45.1%, and mAP0.5:0.95 from 24% to 26.6%. Furthermore, experiments conducted in dynamic real-world traffic scenes illustrated SOD-YOLOv8’s significant enhancements across diverse environmental conditions, highlighting its reliability and effective object detection capabilities in challenging scenarios. Full article
(This article belongs to the Special Issue Advancements in Wireless Localization: Enhancing Object Detection, Navigation and Communications)
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35 pages, 6653 KiB  
Article
Ada-LT IP: Functional Discriminant Analysis of Feature Extraction for Adaptive Long-Term Wi-Fi Indoor Localization in Evolving Environments
by Tesfay Gidey Hailu, Xiansheng Guo, Haonan Si, Lin Li and Yukun Zhang
Sensors 2024, 24(17), 5665; https://doi.org/10.3390/s24175665 - 30 Aug 2024
Cited by 1 | Viewed by 895
Abstract
Wi-Fi fingerprint-based indoor localization methods are effective in static environments but encounter challenges in dynamic, real-world scenarios due to evolving fingerprint patterns and feature spaces. This study investigates the temporal variations in signal strength over a 25-month period to enhance adaptive long-term Wi-Fi [...] Read more.
Wi-Fi fingerprint-based indoor localization methods are effective in static environments but encounter challenges in dynamic, real-world scenarios due to evolving fingerprint patterns and feature spaces. This study investigates the temporal variations in signal strength over a 25-month period to enhance adaptive long-term Wi-Fi localization. Key aspects explored include the significance of signal features, the effects of sampling fluctuations, and overall accuracy measured by mean absolute error. Techniques such as mean-based feature selection, principal component analysis (PCA), and functional discriminant analysis (FDA) were employed to analyze signal features. The proposed algorithm, Ada-LT IP, which incorporates data reduction and transfer learning, shows improved accuracy compared to state-of-the-art methods evaluated in the study. Additionally, the study addresses multicollinearity through PCA and covariance analysis, revealing a reduction in computational complexity and enhanced accuracy for the proposed method, thereby providing valuable insights for improving adaptive long-term Wi-Fi indoor localization systems. Full article
(This article belongs to the Special Issue Advancements in Wireless Localization: Enhancing Object Detection, Navigation and Communications)
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