Radar-Spectrogram-Based UAV Classification Using Convolutional Neural Networks
| Autor: | Seungeui Lee, Seonguk Park, Dongsuk Park, Nojun Kwak |
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| Rok vydání: | 2020 |
| Předmět: |
STFT
Computer science UAV Stability (learning theory) 02 engineering and technology FMCW radar lcsh:Chemical technology Biochemistry Convolutional neural network Article Analytical Chemistry law.invention spectrogram law 020204 information systems MDS 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 Electrical and Electronic Engineering Radar Instrumentation business.industry Deep learning Short-time Fourier transform 020206 networking & telecommunications Pattern recognition Atomic and Molecular Physics and Optics Continuous-wave radar Identification (information) classification Spectrogram Artificial intelligence business CNN |
| Zdroj: | Sensors, Vol 21, Iss 210, p 210 (2021) Sensors (Basel, Switzerland) Sensors Volume 21 Issue 1 |
| ISSN: | 1424-8220 |
| Popis: | With the upsurge in the use of Unmanned Aerial Vehicles (UAVs) in various fields, detecting and identifying them in real-time are becoming important topics. However, the identification of UAVs is difficult due to their characteristics such as low altitude, slow speed, and small radar cross-section (LSS). With the existing deterministic approach, the algorithm becomes complex and requires a large number of computations, making it unsuitable for real-time systems. Hence, effective alternatives enabling real-time identification of these new threats are needed. Deep learning-based classification models learn features from data by themselves and have shown outstanding performance in computer vision tasks. In this paper, we propose a deep learning-based classification model that learns the micro-Doppler signatures (MDS) of targets represented on radar spectrogram images. To enable this, first, we recorded five LSS targets (three types of UAVs and two different types of human activities) with a frequency modulated continuous wave (FMCW) radar in various scenarios. Then, we converted signals into spectrograms in the form of images by Short time Fourier transform (STFT). After the data refinement and augmentation, we made our own radar spectrogram dataset. Secondly, we analyzed characteristics of the radar spectrogram dataset with the ResNet-18 model and designed the ResNet-SP model with less computation, higher accuracy and stability based on the ResNet-18 model. The results show that the proposed ResNet-SP has a training time of 242 s and an accuracy of 83.39%, which is superior to the ResNet-18 that takes 640 s for training with an accuracy of 79.88%. |
| Databáze: | OpenAIRE |
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