Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Bing HAN
 -- 1345 2024-01-31 14:47:19 |
2 update references and layout
Rita Xu
 Meta information modification 1345 2024-02-01 02:58:39 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Yan, X.; Han, B.; Su, Z.; Hao, J. Hybrid Transformer for Automatic Drone Identification. Encyclopedia. Available online: https://encyclopedia.pub/entry/54596 (accessed on 19 May 2024).
Yan X, Han B, Su Z, Hao J. Hybrid Transformer for Automatic Drone Identification. Encyclopedia. Available at: https://encyclopedia.pub/entry/54596. Accessed May 19, 2024.
Yan, Xiang, Bing Han, Zhigang Su, Jingtang Hao. "Hybrid Transformer for Automatic Drone Identification" Encyclopedia, https://encyclopedia.pub/entry/54596 (accessed May 19, 2024).
Yan, X., Han, B., Su, Z., & Hao, J. (2024, January 31). Hybrid Transformer for Automatic Drone Identification. In Encyclopedia. https://encyclopedia.pub/entry/54596
Yan, Xiang, et al. "Hybrid Transformer for Automatic Drone Identification." Encyclopedia. Web. 31 January, 2024.
Hybrid Transformer for Automatic Drone Identification
Edit
This entry is adapted from the peer-reviewed paper 10.3390/s23229098

With the growing integration of drones into various civilian applications, the demand for effective automatic drone identification (ADI) technology has become essential to monitor malicious drone flights and mitigate potential threats.

internet of drones automatic drone identification time–frequency analysis

1. Introduction

With the rapid explosive growth of drone applications in various civilian fields, it is anticipated that millions of drones will access low-altitude airspace, executing diverse civil services within the coming decade [1][2]. However, the surge in drone flights has raised concerns regarding the adequacy of existing air traffic management technologies to ensure the safety and security of low-altitude airspace [3].
To address this challenge, the concept of the Internet of Drones (IoD) has emerged, aimed at enhancing communication, navigation, and surveillance capabilities while concurrently integrating drone flight management [4][5][6]. Automatic drone identification (ADI) [7] is the essential component of the IoD framework to ascertain the presence of drones. ADI technology can be divided into two main categories: active and non-cooperative. Active ADI technology primarily involves detecting drone targets through active radar echoes [8][9]. In contrast, non-cooperative ADI technology passively detects drone targets based on physical mediums such as acoustic signals [7][10], optical signals [11][12], and radio frequency (RF) signals emitted by drones [13][14][15]. Compared to other technologies, non-cooperative ADI technology based on drone RF signals offers a wider surveillance range and higher identification accuracy.
The problem of drone RF signal identification has typically been formulated as a classification problem in the fields of machine learning (ML) and deep learning (DL), which involves using ML or DL models to identify the presence of drone RF signals in the spatial electromagnetic spectrum, thereby determining the existence of drone activity in the airspace. Therefore, from the perspective of the adopted models, previous research can be divided into two main types: ML-based identification models [16][17][18] and DL-based identification models [13][14][19][20][21][22]. ML-based identification models mostly design the handcraft features from the one-dimensional (1D) time-domain waveform of drone RF signals by statistical knowledge, then use ML techniques, such as support vector machine (SVM) or multi-layer perception (MLP), to perform the final classification task by the handcraft features. On the other hand, DL-based identification models primarily employ a deep neural network (DNN) to automatically extract signal features from the 1D time-domain, 1D frequency-domain, or two-dimensional (2D) time–frequency (T-F) domain; they then use MLP to accomplish the classification task.
With the continuous development of deep learning algorithms in recent years and the improvement in computing hardware, more research has adopted the DL-based identification approach, thereby eliminating the dependence of model performance on the quality of handcraft features. In the early stages, the DL-based identification model also relied on automatically extracting features from the time-domain waveform to accomplish drone RF signal identification [19]. While time-domain methods are proficient in effectively handling waveform identifications, they are susceptible to disruptions caused by noise components. Due to the powerful 2D feature extraction capabilities of modern DNNs, some studies suggest first using T-F analysis algorithms to transform signals from the time domain to the T-F domain, then extracting features from both time and frequency dimensions [23][24]. This approach prevents the identification model from being affected by noise outside the signal’s frequency band, thereby enhancing the model’s noise resistance and elevating better identification accuracy.
However, several challenges remain unresolved in previous research, including model architectural designs and engineering applications. Firstly, traditional drone RF signal identification models predominantly rely on convolutional operators, which are local feature extraction algorithms with limited receptive fields, thereby impacting the model’s identification accuracy. Secondly, previous research usually focused on the magnitude information in the T-F spectrum and neglected the use of phase information. Next, earlier studies mainly considered drone RF signal identification in Gaussian white noise environments. However, co-frequency interference, such as Wi-Fi and Bluetooth, can also introduce significant complexities to drone RF signal identification that cannot be overlooked in practical applications. Lastly, due to the data-driven nature of DL-based drone identification methods, well-trained signal identification models are usually effective in identifying signal categories covered by the dataset. Nevertheless, the identification performance may decrease when faced with drone signal categories not included in the dataset. Therefore, it is essential to assess the class-incremental learning (CIL) capability of drone signal identification models.

2. ML-Based Drone RF Signal Identification Techniques

Most of the ML-based techniques rely on manually constructing statistical features from the time-domain waveform and utilizing ML algorithms for signal identification. Experimental results indicate that such algorithms typically have lower computational complexity, but the performance of these algorithms is often limited by identification accuracy. However, due to susceptibility to noise influence in the signal waveform, the performance of these algorithms is often limited by identification accuracy.
In [16], 15 types of statistical features, such as mean, standard deviation, and entropy, are chosen as the basis for identification. Subsequently, the neighborhood component analysis (NCA) algorithm is applied to reduce feature dimensionality. The reduced features are employed to train three machine learning algorithms, namely discriminant analysis (DA), support vector machine (SVM), and neural network (NN). The experimental results indicate that the ML algorithms can exhibit 90% high accuracy when the signal-to-noise ratio (SNR) exceeds 10 dB. In [17], the use of fractal dimension (FD), axially integrated bispectra (AIB), and square-integrated bispectra (SIB) as three types of RF fingerprint features is proposed to replace commonly used statistical measures and enhance the applicability and reliability of feature data. With the help of improved RF fingerprint features, the system shows an accuracy of 100% when two types of drone are identified for SNR 0 dB.
To facilitate the identification of mini drones using Wi-Fi as their communication system, the algorithm proposed in [18] involves extracting statistical features such as packet length and inter-arrival time from Wi-Fi traffic. The identification process employs the cross-entropy function as the loss function, and the maximum likelihood estimation method is applied to estimate the parameters of the exponential distribution. This approach achieves effective drone signal detection with accuracy ranging from 87% to 95% at distances of 70 m and 40 m in line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, respectively.

3. DL-Based Drone RF Signal Identification Techniques

In recent years, with the increase in data volume and the growing complexity of tasks, the field of drone control signal identification has gradually shifted toward using deep neural networks instead of traditional ML algorithms. In addition to processing signals in the time domain, some recent research work has proposed T-F domain identification methods, which involve transforming time-domain signals into the T-F domain for signal identification.
In [19], an auxiliary classifier Wasserstein generative adversarial network (AC-WGANs) is utilized for recognizing drone temporal waveforms. To mitigate computational complexity, the authors preprocess and dimensionally reduce the received signal waveforms, representing information in a lower-dimensional space. Subsequently, the processed signal data are input into the AC-WGANs model for feature extraction and signal identification. Experimental results indicate that the model achieves approximately 95% identification accuracy for SNR 5 dB. In [20], an end-to-end signal detection and identification model is proposed to save computation time during the feature extraction step. The SqueezeNet model with one-dimensional convolution operators is used to directly extract RF fingerprint features from the time-domain envelope. This approach significantly reduces the model’s computation latency, with an inference time of only 0.37 ms for a single drone RF signal. Within the 0 dB to 30 dB SNR range, the method achieves an average identification accuracy of 97.53%. However, as the SNR drops to 0 dB, the model’s identification performance is notably compromised due to the impact of noise impact.
In [21], the drone signal undergoes the short-time Fourier transform (STFT) algorithm to derive the T-F spectrum. Subsequently, a residual neural network (ResNet) is applied to extract feature information from the T-F spectrum. The authors assessed the algorithm’s performance in drone RF signal detection across various SNRs. The system attains nearly 99% identification accuracy at an SNR of 0 dB. In [22], the authors proposed utilizing wavelet transform analysis for the time–frequency domain transformation of drone signals. They also compared the feature extraction performance of three wavelet transform algorithms: discrete wavelet transform (DWT), continuous wavelet transform (CWT), and wavelet scattering transform (WST). Among them, the identification method based on WST and SqueezeNet demonstrated superior performance, achieving an accuracy of 98.9% at an SNR of 10 dB.

References

  1. Outay, F.; Mengash, H.A.; Adnan, M. Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges. Transp. Res. Part A Policy Pract. 2020, 141, 116–129.
  2. Arishi, A.; Krishnan, K.; Arishi, M. Machine learning approach for truck-drones based last-mile delivery in the era of industry 4.0. Eng. Appl. Artif. Intell. 2022, 116, 105439.
  3. Dahle, O.H.; Rydberg, J.; Dullweber, M.; Peinecke, N.; Bechina, A.A.A. A proposal for a common metric for drone traffic density. In Proceedings of the 2022 International Conference on Unmanned Aircraft Systems (ICUAS), Dubrovnik, Croatia, 21–24 June 2022; pp. 64–72.
  4. Gharibi, M.; Boutaba, R.; Waslander, S.L. Internet of drones. IEEE Access 2016, 4, 1148–1162.
  5. Ntizikira, E.; Lei, W.; Alblehai, F.; Saleem, K.; Lodhi, M.A. Secure and Privacy-Preserving Intrusion Detection and Prevention in the Internet of Unmanned Aerial Vehicles. Sensors 2023, 23, 8077.
  6. Svaigen, A.R.; Boukerche, A.; Ruiz, L.B.; Loureiro, A.A. Security in the Industrial Internet of Drones. IEEE Internet Things Mag. 2023, 6, 110–116.
  7. Svaigen, A.R.; Bine, L.M.; Pappa, G.L.; Ruiz, L.B.; Loureiro, A.A. Automatic drone identification through rhythm-based features for the internet of drones. In Proceedings of the 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), Washington, DC, USA, 1–3 November 2021; pp. 1417–1421.
  8. Mittal, P.; Singh, R.; Sharma, A. Deep learning-based object detection in low-altitude UAV datasets: A survey. Image Vis. Comput. 2020, 104, 104046.
  9. Jian, M.; Lu, Z.; Chen, V.C. Drone detection and tracking based on phase-interferometric Doppler radar. In Proceedings of the 2018 IEEE Radar Conference (RadarConf18), Oklahoma City, OK, USA, 23–27 April 2018; pp. 1146–1149.
  10. Kolamunna, H.; Dahanayaka, T.; Li, J.; Seneviratne, S.; Thilakaratne, K.; Zomaya, A.Y.; Seneviratne, A. Droneprint: Acoustic signatures for open-set drone detection and identification with online data. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2021, 5, 1–31.
  11. Wu, M.; Xie, W.; Shi, X.; Shao, P.; Shi, Z. Real-time drone detection using deep learning approach. In Proceedings of the Machine Learning and Intelligent Communications: Third International Conference, MLICOM 2018, Hangzhou, China, 6–8 July 2018; Proceedings 3. Springer International Publishing: Berlin/Heidelberg, Germany, 2018; pp. 22–32.
  12. Svanström, F.; Englund, C.; Alonso-Fernandez, F. Real-time drone detection and tracking with visible, thermal and acoustic sensors. In Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 10–15 January 2021; pp. 7265–7272.
  13. Liang, H.; Wang, R.; Xu, M.; Zhou, F.; Wu, Q.; Dobre, O.A. Few-Shot Learning UAV Recognition Methods Based on the Tri-Residual Semantic Network. IEEE Commun. Lett. 2022, 26, 2072–2076.
  14. Han, B.; Yan, X.; Su, Z.; Hao, J. Automated Drone Classification for Internet of Drones Based on a Hybrid Transformer Model. In Proceedings of the 2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI), Macao, China, 31 October–2 November 2022; pp. 812–817.
  15. Medaiyese, O.O.; Ezuma, M.; Lauf, A.P.; Guvenc, I. Semi-supervised learning framework for UAV detection. In Proceedings of the 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Helsinki, Finland, 13–16 September 2021; pp. 1185–1190.
  16. Ezuma, M.; Erden, F.; Anjinappa, C.K.; Ozdemir, O.; Guvenc, I. Detection and classification of UAVs using RF fingerprints in the presence of Wi-Fi and Bluetooth interference. IEEE Open J. Commun. Soc. 2019, 1, 60–76.
  17. Nie, W.; Han, Z.C.; Zhou, M.; Xie, L.B.; Jiang, Q. UAV detection and identification based on WiFi signal and RF fingerprint. IEEE Sens. J. 2021, 21, 13540–13550.
  18. Alipour-Fanid, A.; Dabaghchian, M.; Wang, N.; Wang, P.; Zhao, L.; Zeng, K. Machine learning-based delay-aware UAV detection and operation mode identification over encrypted Wi-Fi traffic. IEEE Trans. Inf. Forensics Secur. 2019, 15, 2346–2360.
  19. Zhao, C.; Chen, C.; Cai, Z.; Shi, M.; Du, X.; Guizani, M. Classification of small UAVs based on auxiliary classifier Wasserstein GANs. In Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, 9–13 December 2018; pp. 206–212.
  20. Alam, S.S.; Chakma, A.; Rahman, M.H.; Bin Mofidul, R.; Alam, M.M.; Utama, I.B.K.Y.; Jang, Y.M. RF-Enabled Deep-Learning-Assisted Drone Detection and Identification: An End-to-End Approach. Sensors 2023, 23, 4202.
  21. Basak, S.; Rajendran, S.; Pollin, S.; Scheers, B. Drone classification from RF fingerprints using deep residual nets. In Proceedings of the 2021 International Conference on COMmunication Systems & NETworkS (COMSNETS), Bangalore, India, 5–9 January 2021; pp. 548–555.
  22. Medaiyese, O.O.; Ezuma, M.; Lauf, A.P.; Guvenc, I. Wavelet transform analytics for RF-based UAV detection and identification system using machine learning. Pervasive Mob. Comput. 2022, 82, 101569.
  23. Mohanti, S.; Soltani, N.; Sankhe, K.; Jaisinghani, D.; Di Felice, M.; Chowdhury, K. AirID: Injecting a custom RF fingerprint for enhanced UAV identification using deep learning. In Proceedings of the GLOBECOM 2020-2020 IEEE Global Communications Conference, Taipei, Taiwan, 7–11 December 2020; pp. 1–6.
  24. Ozturk, E.; Erden, F.; Guvenc, I. RF-based low-SNR classification of UAVs using convolutional neural networks. arXiv 2020, arXiv:2009.05519.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Engineering, Aerospace
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Xiang Yan
,
Bing Han
,
Zhigang Su
,
Jingtang Hao
View Times: 100
Revisions: 2 times (View History)
Update Date: 01 Feb 2024
Table of Contents
    1000/1000
    Hot Most Recent
    Feedback