Hybrid Quantum Classical Neural Network for radar data

We want to classify drones based on RADAR signals using convolutional neural networks and using hybrid quantum neural networks. We also create a classical annd hybrid quantum model for binary classification for radar detection. The preprint of this work is available in https://arxiv.org/abs/2403.02080.

Data Generation

The dataset of STFT spectrograms of drone signals are generated based on the Martin-Mulgrew model [1] in DataGeneration.ipynb.

Classifier Models

The Classical convoution neural network (CNN) used to classify drones from radar signals is available in Classical_Drone_Classifier.ipynb. The Hybrid Quantum Neural Network (CNN) used to classify drones from radar signals is available in Hybrid_Drone_Classifier.ipynb.

Detector Models

The Classical convoution neural network (CNN) used to detect drones (binary classification) from radar signals is available in Classical_Drone_Binary_Classifier.ipynb. The Hybrid Quantum Neural Network (CNN) used to detect drones (binary classification) from radar signals is available in Hybrid_Drone_Binary_Classifier.ipynb.

Referenence

[1] J. Martin and B. Mulgrew, 'Analysis of the effects of blade pitch on the radar return signal from rotating aircraft blades', in 92 International Conference on Radar, Oct. 1992, pp. 446–449.