Deep learning approach for investigation of temporal radio frequency signatures of drones

Sohal, Rajdeep Singh; Grewal, Vinit; Singh, Karamjit; Kaur, Jaipreet

doi:10.1002/dac.5377

Cited by 3 publications

(1 citation statement)

References 35 publications

(130 reference statements)

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“…[50] Used own datasets with seven DJI drones; 500 spectrograms are generated; 90% used for training and 10% for validation. [51] Used DroneRF datasets [57] where out of 22,700 × 7 elements 90%, 20,430 × 7, was used for training and 2270 × 7 used for testing datasets. [52] Used own experimental datasets with trained PSD models for 6 UAVs where used 178 data points for every training example.…”

Section: Uav Classification Based On ML Using Rf Analysismentioning

confidence: 99%

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Rahman,

Sejan,

Aziz

et al. 2024

Remote Sensing

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Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches.

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Section: Uav Classification Based On ML Using Rf Analysismentioning

confidence: 99%

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Rahman,

Sejan,

Aziz

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

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Hierarchical RNNs with graph policy and attention for drone swarm

Wei,

Cui,

Huang

et al. 2024

Journal of Computational Design and Engineering

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In recent years, the drone swarm has experienced remarkable growth, finding applications across diverse domains such as agricultural surveying, disaster rescue, and logistics delivery. However, the rapid expansion of drone swarm usage underscores the necessity for innovative approaches in the field. Traditional algorithms face challenges in adapting to complex tasks, environmental modeling, and computational complexity, highlighting the need for more advanced solutions like multi-agent deep reinforcement learning to enhance efficiency and robustness in drone swarm. Our proposed approach tackles this challenge by embracing temporal and spatial. In terms of the temporal, the proposed approach builds upon historical data, it enhances the predictive capabilities regarding future behaviors. In the spatial, the proposed approach leverage graph theory to model the swarm's features, while attention mechanisms strengthen the relationships between individual drones. The proposed approach addresses the unique characteristics of drone swarms by incorporating temporal dependencies, spatial structures, and attention mechanisms. Extensive experiments validate the effectiveness of the proposed approach.

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Improved RF-Based Drone Detection via Advanced Feature Extraction and Sigmoid Calibration

Jararweh,

Daraghmeh,

Agarwal

2024

NAECON 2024 - IEEE National Aerospace and Electronics Conference

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Deep learning approach for investigation of temporal radio frequency signatures of drones

Cited by 3 publications

References 35 publications

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Hierarchical RNNs with graph policy and attention for drone swarm

Improved RF-Based Drone Detection via Advanced Feature Extraction and Sigmoid Calibration

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