Abstract
The ability to classify drones using radar signals is a problem of great interest. In this paper, we apply convolutional neural networks (CNNs) to the Short-Time Fourier Transform (STFT) spectrograms of the simulated radar signals reflected from the drones. The drones vary in many ways that impact the STFT spectrograms, including blade length and blade rotation rates. Some of these physical parameters are captured in the Martin and Mulgrew model which was used to produce the datasets. We examine the data under X-band and W-band radar simulation scenarios and show that a CNN approach leads to an F1 score of 0.816±0.011 when trained on data with a signal-to-noise ratio (SNR) of 10 dB. The neural network which was trained on data from an X-band radar with 2 kHz pulse repetition frequency was shown to perform better than the CNN trained on the aforementioned W-band radar. It remained robust to the drone blade pitch and its performance varied directly in a linear fashion with the SNR.
Highlights
Modern drones are more affordable than ever, and their uses extend into many industries such as emergency response, disease control, weather forecasting, and journalism [1].Their increased military use and the possible weaponization of drones have caused drone detection and identification to be an important matter of public safety.There are several types of technology which can facilitate drone detection and classification
We show it is possible to train convolutional neural networks (CNNs) classifiers robust to signal-to-noise ratio (SNR)-levels not included in training while maintaining performance that is invariant to the blade pitch of the drones
A W-band radar with a 2 kHz pulse repetition frequency (PRF) was trained as a control model, which demonstrated weaker performance against the X-band radar at the same PRF
Summary
Modern drones are more affordable than ever, and their uses extend into many industries such as emergency response, disease control, weather forecasting, and journalism [1].Their increased military use and the possible weaponization of drones have caused drone detection and identification to be an important matter of public safety.There are several types of technology which can facilitate drone detection and classification. Modern drones are more affordable than ever, and their uses extend into many industries such as emergency response, disease control, weather forecasting, and journalism [1]. Their increased military use and the possible weaponization of drones have caused drone detection and identification to be an important matter of public safety. Drones give off a unique acoustic signature ranging from 400 Hz to 8 kHz, and microphones can capture this information. This technology can only be used at a maximum range of 10 meters, and the microphones are sensitive to environmental noise [2]
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