Drone Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks is a research paper by Alan Frid, Yehuda Ben-Shimol, Erez Manor, and Shlomo Greenberg.

The rising popularity of drones across various sectors, from aerial photography to agriculture, brings numerous benefits. However, the potential misuse of drone technology poses significant risks, including military threats, terrorism, and privacy breaches. This underscores the urgent need for effective and swift detection of potentially hazardous drones.

This study proposes a novel method for automatic drone detection by leveraging both radio frequency (RF) communication signals and acoustic signals from UAV rotor sounds. This study advocates using classical and deep machine-learning techniques, combined with the fusion of RF and acoustic features, to enhance the efficiency and accuracy of drone classification. The study explored different ML-based classifiers, such as CNN- and RNN-based networks, along with the traditional SVM method.

The proposed approach is evaluated using standard drone datasets, incorporating both frequency and audio features. The results demonstrate superior accuracy compared to existing methods, particularly in scenarios with low signal-to-noise ratios (SNR). The study’s approach achieved a classification accuracy of approximately 91% at an SNR ratio of -10 dB using the LSTM network and fused features.

Publication Date– April 2024

Drone Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks contains the following major sections:

  • Introduction
  • Related Work
  • Proposed Approach
  • Results
  • Summary and Conclusions

All articles published by MDPI are immediately available worldwide under an open-access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. Any part of the article may be reused without the consent for articles published under an open-access Creative Common CC BY license, provided the original article is cited. For more information, please refer to https://www.mdpi.com/openaccess.

Post Image Credit- envatoelements by Stock87