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http://dx.doi.org/10.6109/jkiice.2021.25.5.619

Machine learning based radar imaging algorithm for drone detection and classification  

Moon, Min-Jung (Department of Electronic Engineering, Korea Aerospace University)
Lee, Woo-Kyung (Department of Electronic Engineering, Korea Aerospace University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.5, 2021 , pp. 619-627 More about this Journal
Abstract
Recent advance in low cost and light-weight drones has extended their application areas in both military and private sectors. Accordingly surveillance program against unfriendly drones has become an important issue. Drone detection and classification technique has long been emphasized in order to prevent attacks or accidents by commercial drones in urban areas. Most commercial drones have small sizes and low reflection and hence typical sensors that use acoustic, infrared, or radar signals exhibit limited performances. Recently, artificial intelligence algorithm has been actively exploited to enhance radar image identification performance. In this paper, we adopt machined learning algorithm for high resolution radar imaging in drone detection and classification applications. For this purpose, simulation is carried out against commercial drone models and compared with experimental data obtained through high resolution radar field test.
Keywords
Radar; ISAR; Micro-doppler; Machine learning; CNN;
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  • Reference
1 S. Bjorklund, "Target detection and classification of small drones by boosting on radar micro-doppler," 2018 15th European Radar Conference (EuRAD), IEEE, pp. 182-185, 2018.
2 S. Rahman and D. A. Robertson, "Radar micro-Doppler signatures of drones and birds at K-band and W-band," Scientific Reports, vol. 8, no. 1, pp. 1-11, 2018.
3 F. Hoffmann, M. Ritchie, and F. Fioranelli, "Micro-Doppler based detection and tracking of UAVs with multistatic radar," 2016 IEEE Radar Conference (RadarConf), IEEE, pp. 1-6, 2016.
4 B. Taha and A. Shoufan, "Machine learning-based drone detection and classification: State-of-the-art in research," IEEE Access, vol. 7, pp. 138669-138682, 2019.   DOI
5 P. Klaer, A. Huang, P. Sevigny, S. Rajan., S. Pant, P. Patnaik, and B. Balaji, "An Investigation of Rotary Drone HERM Line Spectrum under Manoeuvering Conditions," Sensors2020, vol. 20, no. 20, pp. 5940, 2020.   DOI
6 M. Ezuma, F. Erden, C. K. Anjinappa, O. Ozdemir, and I. Guvenc, "Micro-UAV detection and classification from RF fingerprints using machine learning techniques," 2019 IEEE Aerospace Conference, IEEE, pp. 1-13, 2019.
7 H. Kang, B. K. Kim, J. S. Park, J. S. Suh, and S. O. Park, "Drone Elevation Angle Classification Based on Convolutional Neural Network With Micro-Doppler of Multipolarization," IEEE Geoscience and Remote Sensing Letters, 2020.
8 D. A. Brooks, O. Schwander, F. Barbaresco, J. Y. Schneider, and M. Cord "Temporal deep learning for drone microDoppler classification," 2018 19th International Radar Symposium (IRS), pp. 1-10, 2018.
9 S. Rahman and D. A. Robertson "Classification of drones and birds using convolutional neural networks applied to radar micro-Doppler spectrogram images," IET Radar, Sonar & Navigation, vol. 14, no. 5, pp. 653-661, 2019.
10 K. M. Song, M. J. Moon, and W. K. Lee, "Experimental Study of Drone Detection and Classification through FMCW ISAR and CW Micro-Doppler Analysis," Korea institute of millitary science and technology, vol. 21, no. 2, pp. 147-157, 2018.
11 P. M. Radiuk, "Impact of training set batch size on the performance of convolutional neural networks for diverse datasets," Information Technology and Management Science, vol. 21, no. 1, pp. 20-24, 2017.
12 S. Rahman and D. A. Robertson, "Multiple drone classification using millimeter-wave CW radar micro-Doppler data," Radar Sensor Technology XXIV., International Society for Optics and Photonics, vol. 11408, pp. 1140809, 2020.