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Classification of Flying Drones Using Millimeter-Wave Radar: Comparative Analysis of Algorithms Under Noisy

Mauro Larrat1, Claudomiro Sales1

  • 1Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Rua Augusto Corrêa, 01 Guamá, CEP, Belém 66075-110, PA, Brazil.

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This study enhances drone detection using machine learning on radar data. A Multimodal Transformer model improves accuracy, especially in noisy conditions, outperforming standard algorithms.

Keywords:
drone detectionmachine learningmillimeter-wave radar sensor

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Area of Science:

  • Radar Signal Processing
  • Machine Learning Applications
  • Unmanned Aerial Vehicle (UAV) Detection

Background:

  • Radar systems are crucial for detecting objects like drones.
  • Environmental noise significantly impacts the performance of radar detection algorithms.
  • Existing machine learning models face challenges in robust drone identification under diverse noise conditions.

Purpose of the Study:

  • To evaluate and benchmark machine learning algorithms for drone detection using 60 GHz millimeter-wave radar data.
  • To investigate the robustness of different algorithms against various noise types.
  • To propose an improved model for enhanced drone and bird detection accuracy.

Main Methods:

  • Collected radar data from a bionic bird and two drones (DJI Mavic, DJI Phantom 3 Pro).
  • Benchmarked Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (Conv1D), and Transformer algorithms.
  • Introduced a Multimodal Transformer incorporating statistical features (skewness, kurtosis) alongside amplitude and phase data.

Main Results:

  • Transformer showed high accuracy but had weaknesses in specific noise types like Pareto noise.
  • The proposed Multimodal Transformer demonstrated improved detection accuracy, particularly under challenging noise conditions.
  • Results highlight the critical role of noise and the advantage of multimodal data presentation.

Conclusions:

  • Machine learning algorithms, especially advanced ones like Transformers, are effective for radar-based drone detection.
  • Data augmentation with statistical features (Multimodal Transformer) significantly enhances algorithm robustness against noise.
  • This study provides a benchmark for radar detection systems and insights into noise-resilient methodologies.