RF-Enabled Deep-Learning-Assisted Drone Detection and Identification: An End-to-End Approach is a study by Syed Samiul Alam, Md Habibur Rahman, Raihan Bin Mofidul, Md Morshed Alam, Ida Bagus Krishna Yoga Utama, and Yeong Min Jang.

This article addresses the rising security concerns related to unmanned aerial vehicles (UAVs) and proposes an end-to-end deep-learning-based model for their detection and identification based on radio frequency (RF) signatures. The model utilizes multiscale feature-extraction techniques without manual intervention, ensuring efficient extraction of enriched features and better generalization capability with lower computational time.

Residual blocks are incorporated to handle complex representations and address vanishing gradient issues during training. The model demonstrates effectiveness in the presence of interfering signals such as Bluetooth and WIFI. Evaluation across various signal-to-noise ratios (SNR) yields impressive results, with an overall accuracy of 97.53%, precision of 98.06%, sensitivity of 98.00%, and F1-score of 98.00% for RF signal detection in the CardRF dataset. The proposed model’s inference time of 0.37 milliseconds outperforms existing work, making it a promising solution for real-time UAV detection and identification in surveillance systems.

Publication Date– April 2023

RF-Enabled Deep-Learning-Assisted Drone Detection and Identification: An End-to-End Approach contains the following major sections:

  • Introduction
  • Methodology
  • Experimental Results
  • Conclusions

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Authors-Syed Samiul Alam, Md Habibur Rahman, Raihan Bin Mofidul, Md Morshed Alam, Ida Bagus Krishna Yoga Utama, and Yeong Min Jang

See Also-

Passive bistatic radar: Target detection and interference

Drone Detection and Tracking Using RF Identification Signals

Deep Learning on Multi-Sensor Data for Counter UAV Applications

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