Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Application of Linearization and Approximation

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Reinforcement Learning Based Topology Control for UAV Networks.

Sensors (Basel, Switzerland)·2023

Same author

Guest Editorial Special Issue on Time-Sensitive Networks for Unmanned Aircraft Systems.

Sensors (Basel, Switzerland)·2021

Same author

An MPTCP-Based Transmission Scheme for Improving the Control Stability of Unmanned Aerial Vehicles.

Sensors (Basel, Switzerland)·2021

Same author

Devising a Distributed Co-Simulator for a Multi-UAV Network.

Sensors (Basel, Switzerland)·2020

Same author

UAV Flight and Landing Guidance System for Emergency Situations <sup>†</sup>.

Sensors (Basel, Switzerland)·2019

Same author

Pedestrian Positioning Using a Double-Stacked Particle Filter in Indoor Wireless Networks.

Sensors (Basel, Switzerland)·2019

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 30, 2026

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

Adaptive Sensing Data Augmentation for Drones Using Attention-Based GAN.

Namkyung Yoon¹, Kiseok Kim¹, Sangmin Lee¹

¹School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea.

Sensors (Basel, Switzerland)

|August 29, 2024

Summary

This summary is machine-generated.

This study introduces a deep learning system using generative adversarial networks (GANs) to create synthetic sensor data for drones. This enhances data collection efficiency and extends drone operational capabilities.

Keywords:

attention mechanism deep learning drone generative adversarial network

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

Related Experiment Videos

Last Updated: Jun 30, 2026

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

Area of Science:

Robotics and Automation
Artificial Intelligence
Data Science

Background:

Drones are vital for real-time data collection but face payload and data management challenges.
Integrating multiple sensors on drones is complex due to hardware limitations.
Drone-collected time-series sensor data often suffers from scarcity and resolution issues.

Purpose of the Study:

To develop a deep learning system for augmenting drone-collected time-series sensor data.
To address data scarcity by generating realistic synthetic data.
To improve the efficiency and performance of drone-based applications.

Main Methods:

Utilized an attention-based generative adversarial network (GAN) for synthetic data generation.
Implemented adaptive sensing frequency adjustment based on operational conditions.
Employed spatiotemporal attention mechanisms within the GAN to enhance data realism.

Main Results:

The system effectively generated high-quality synthetic data, filling gaps from reduced sensing frequency.
Demonstrated improved efficiency and performance in applications like precision agriculture and environmental monitoring.
Experimental results confirmed extended operational range and duration for drones with augmented data.

Conclusions:

The proposed attention-based GAN system effectively addresses data scarcity in drone sensor data.
This methodology enhances drone operational capabilities and data reliability.
The system offers a robust solution for various drone-based monitoring and surveillance applications.