Radio Frequency Signal-Based Drone Classification with Frequency Domain Gramian Angular Field and Convolutional Neural Network
Over the past few years, drones have been utilized in a wide range of applications. However, the illegal operation of drones may pose a series of security risks to sensitive areas such as airports and military bases. Hence, it is vital to develop an effective method of identifying drones to address...
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MDPI AG
2024-09-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/9/511 |
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| author | Yuanhua Fu Zhiming He |
| author_facet | Yuanhua Fu Zhiming He |
| author_sort | Yuanhua Fu |
| collection | DOAJ |
| description | Over the past few years, drones have been utilized in a wide range of applications. However, the illegal operation of drones may pose a series of security risks to sensitive areas such as airports and military bases. Hence, it is vital to develop an effective method of identifying drones to address the above issues. Existing drone classification methods based on radio frequency (RF) signals have low accuracy or a high computational cost. In this paper, we propose a novel RF signal image representation scheme that incorporates a convolutional neural network (CNN), named the frequency domain Gramian Angular Field with a CNN (FDGAF-CNN), to perform drone classification. Specifically, we first compute the time–frequency spectrum of raw RF signals based on short-time Fourier transform (STFT). Then, the 1D frequency spectrum series is encoded as 2D images using a modified GAF transform. Moreover, to further improve the recognition performance, the images obtained from different channels are fused to serve as the input of a CNN classifier. Finally, numerous experiments were conducted on the two available open-source DroneRF and DroneRFa datasets. The experimental results show that the proposed FDGAF-CNN can achieve a relatively high classification accuracy of 98.72% and 98.67% on the above two datasets, respectively, confirming the effectiveness and generalization ability of the proposed method. |
| format | Article |
| id | doaj-art-dfd4a40dd1084e31a6d3a7ed2dd5e76f |
| institution | OA Journals |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-dfd4a40dd1084e31a6d3a7ed2dd5e76f2025-08-20T01:55:30ZengMDPI AGDrones2504-446X2024-09-018951110.3390/drones8090511Radio Frequency Signal-Based Drone Classification with Frequency Domain Gramian Angular Field and Convolutional Neural NetworkYuanhua Fu0Zhiming He1School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaInstitute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan 523808, ChinaOver the past few years, drones have been utilized in a wide range of applications. However, the illegal operation of drones may pose a series of security risks to sensitive areas such as airports and military bases. Hence, it is vital to develop an effective method of identifying drones to address the above issues. Existing drone classification methods based on radio frequency (RF) signals have low accuracy or a high computational cost. In this paper, we propose a novel RF signal image representation scheme that incorporates a convolutional neural network (CNN), named the frequency domain Gramian Angular Field with a CNN (FDGAF-CNN), to perform drone classification. Specifically, we first compute the time–frequency spectrum of raw RF signals based on short-time Fourier transform (STFT). Then, the 1D frequency spectrum series is encoded as 2D images using a modified GAF transform. Moreover, to further improve the recognition performance, the images obtained from different channels are fused to serve as the input of a CNN classifier. Finally, numerous experiments were conducted on the two available open-source DroneRF and DroneRFa datasets. The experimental results show that the proposed FDGAF-CNN can achieve a relatively high classification accuracy of 98.72% and 98.67% on the above two datasets, respectively, confirming the effectiveness and generalization ability of the proposed method.https://www.mdpi.com/2504-446X/8/9/511drone classificationRF signal image representationfrequency domain Gramian Angular FieldCNN |
| spellingShingle | Yuanhua Fu Zhiming He Radio Frequency Signal-Based Drone Classification with Frequency Domain Gramian Angular Field and Convolutional Neural Network Drones drone classification RF signal image representation frequency domain Gramian Angular Field CNN |
| title | Radio Frequency Signal-Based Drone Classification with Frequency Domain Gramian Angular Field and Convolutional Neural Network |
| title_full | Radio Frequency Signal-Based Drone Classification with Frequency Domain Gramian Angular Field and Convolutional Neural Network |
| title_fullStr | Radio Frequency Signal-Based Drone Classification with Frequency Domain Gramian Angular Field and Convolutional Neural Network |
| title_full_unstemmed | Radio Frequency Signal-Based Drone Classification with Frequency Domain Gramian Angular Field and Convolutional Neural Network |
| title_short | Radio Frequency Signal-Based Drone Classification with Frequency Domain Gramian Angular Field and Convolutional Neural Network |
| title_sort | radio frequency signal based drone classification with frequency domain gramian angular field and convolutional neural network |
| topic | drone classification RF signal image representation frequency domain Gramian Angular Field CNN |
| url | https://www.mdpi.com/2504-446X/8/9/511 |
| work_keys_str_mv | AT yuanhuafu radiofrequencysignalbaseddroneclassificationwithfrequencydomaingramianangularfieldandconvolutionalneuralnetwork AT zhiminghe radiofrequencysignalbaseddroneclassificationwithfrequencydomaingramianangularfieldandconvolutionalneuralnetwork |