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Intelligent Sensor Systems in Unmanned Aerial Vehicles

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 473

Special Issue Editors


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Guest Editor
1. Academy of Silesia, (Akademia Śląska) Rolna 43, 40-555 Katowice, Poland
2. Department of Graphics, Computer Vision and Digital Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Interests: UAV control; computer vision and graphics; digital systems

E-Mail Website
Guest Editor
Department of Graphics, Computer Vision and Digital Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Interests: UAV control; computer vision and graphics; human gait; artificial intelligence; databases

E-Mail Website
Guest Editor
Department of Graphics, Computer Vision and Digital Systems, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Interests: processing and classification of motion capture data; time series analysis; machine learning; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

While the physical principles behind UAV sensors remain unchanged compared to that of individual sensors, the intelligence of UAV sensor systems arises from their ability to process, interpret, and correlate the data they provide. Intelligent sensor systems do not merely sense the environment—they understand it. This understanding emerges through real-time data fusion, context-aware interpretation, and adaptive decision-making algorithms, which allow UAVs to navigate complex environments, anticipate changes, and coordinate with other platforms.

Relevant sensors include RGB, infrared (IR), monochrome and stereo cameras, multi- and hyperspectral imaging systems, and ultrasonic and ultra-wideband (UWB) sensors, which are being used for localization and obstacle avoidance increasingly frequently.

Inertial measurement units (IMUs) of various precision levels, including those integrated with vision (visual–inertial systems), are essential for UAV pose estimation. Other key pose modalities include magnetometers supported by geomagnetic maps and high-end gyroscopes, such as fiber-optic gyroscopes (FOGs) and ring laser gyroscopes (RLGs).

We welcome original research articles, review papers, and application-focused studies related (but not limited) to the following topics:

  • Novel sensor systems for UAVs (RGB, IR, stereo, multispectral, hyperspectral, ultrasonic, UWB, IMU, magnetometers, gyroscopes, etc.);
  • The calibration and integration of heterogeneous sensors;
  • Sensor fusion and visual–inertial odometry;
  • Pose estimation and autonomous navigation;
  • Sensory data-driven mapping (landmarks, objects, magnetic fields);
  • Algorithms for processing high-dimensional sensor data;
  • Intelligent control based on sensory inputs;
  • Real-world applications of sensor-based UAV platforms;

Prof. Dr. Konrad W. Wojciechowski
Dr. Henryk Josiński
Dr. Adam Świtoński
Guest Editors

Manuscript Submission Information

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Keywords

  • RGB and IR sensors
  • sensor fusion
  • visual–inertial systems
  • hyperspectral imaging
  • ultrasonic sensors
  • sensor calibration
  • magnetometer mapping
  • intelligent navigation

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Published Papers (1 paper)

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Research

12 pages, 1329 KiB  
Article
Steady-State Visual-Evoked-Potential–Driven Quadrotor Control Using a Deep Residual CNN for Short-Time Signal Classification
by Jiannan Chen, Chenju Yang, Rao Wei, Changchun Hua, Dianrui Mu and Fuchun Sun
Sensors 2025, 25(15), 4779; https://doi.org/10.3390/s25154779 - 3 Aug 2025
Viewed by 311
Abstract
In this paper, we study the classification problem of short-time-window steady-state visual evoked potentials (SSVEPs) and propose a novel deep convolutional network named EEGResNet based on the idea of residual connection to further improve the classification performance. Since the frequency-domain features extracted from [...] Read more.
In this paper, we study the classification problem of short-time-window steady-state visual evoked potentials (SSVEPs) and propose a novel deep convolutional network named EEGResNet based on the idea of residual connection to further improve the classification performance. Since the frequency-domain features extracted from short-time-window signals are difficult to distinguish, the EEGResNet starts from the filter bank (FB)-based feature extraction module in the time domain. The FB designed in this paper is composed of four sixth-order Butterworth filters with different bandpass ranges, and the four bandwidths are 19–50 Hz, 14–38 Hz, 9–26 Hz, and 3–14 Hz, respectively. Then, the extracted four feature tensors with the same shape are directly aggregated together. Furthermore, the aggregated features are further learned by a six-layer convolutional neural network with residual connections. Finally, the network output is generated through an adaptive fully connected layer. To prove the effectiveness and superiority of our designed EEGResNet, necessary experiments and comparisons are conducted over two large public datasets. To further verify the application potential of the trained network, a virtual simulation of brain computer interface (BCI) based quadrotor control is presented through V-REP. Full article
(This article belongs to the Special Issue Intelligent Sensor Systems in Unmanned Aerial Vehicles)
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