Classification of birds and drones by exploiting periodical motions in Doppler spectrum series

doi:10.23919/JSEE.2023.000002

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (1): 19-27.doi: 10.23919/JSEE.2023.000002

• REMOTE SENSING • Previous Articles Next Articles

Classification of birds and drones by exploiting periodical motions in Doppler spectrum series

Jia DUAN¹(), Lei ZHANG¹(), Yifeng WU¹(), Yue ZHANG¹^,*(), Zeya ZHAO²(), Xinrong GUO³()

¹ School of Electronics and Communication, Sun Yat-Sen University, Shenzhen 518107, China
² Beijing Institute of Tracking and Telecommunication Technology, Beijing 100854, China
³ Armed Police Engineering University, Xi’an 510507, China

Received:2022-04-07 Accepted:2022-10-18 Online:2023-02-18 Published:2023-03-03
Contact: Yue ZHANG E-mail:bifiduan119@126.com;zhanglei57@mail.sysu.edu.cn;wuyf95@mail.sysu.edu.cn;zhangyue_frog1@163.com;zzy_kelly@163.com;rapgxr@163.com
About author:
DUAN Jia was born in 1989. She received her Ph.D. degree in signal processing from Xidian University in 2015. She worked for the Chinese Aviation Industry Corporation as a senior engineer in signal processing and target recognition from 2016 to 2021. Afterward, she works as an associate research fellow in the School of Electronics and Communication, Sun Yat-sen University. Her research interests include radar signal processing, ISAR imaging, SAR/ISAR feature extraction and target recognition. E-mail: bifiduan119@126.com

ZHANG Lei was born in 1984. He received his B.S. degree from Chang’an University in 2006. He received his Ph.D. degree from Xidian University in 2011. He worked in the National Key Lab of Radar Signal Processing from 2011 to 2019. Currently, he works as a professor in the School of Electronics and Communication, Sun Yat-sen University. His research interests include radar signal processing, SAR/ISAR imaging, SAR interpreting, electronic counter measures, etc. E-mail: zhanglei57@mail.sysu.edu.cn

WU Yifeng was born in 1988. He received his B.S. and Ph.D. degrees from Xidian University in 2010 and 2016, respectively. He worked for the Chinese Aviation Industry Corporation as a senior engineer in signal processing and target detection from 2016 to 2020. Currently, He works as an associate professor in the School of Electronics and Communication, Sun Yat-sen University. His research interests include signal processing, space-time adaptive processing, radar target detection, and clutter suppression. E-mail: wuyf95@mail.sysu.edu.cn

ZHANG Yue was born in 1980. He received his Ph.D. degree in electronic science and technology at National University of Defense Technology. He is now an associate professor in Sun Yat-sen University. His research interests include radar signal processing and automatic targets recognition. E-mail: zhangyue_frog1@163.com

ZHAO Zeya received her M.S. degree in information engineering from Information Engineering University. She is now with Beijing Institute of Tracking and Transmission Technology. Her research interests include radar signal analysis and intelligence information processing. E-mail: zzy_kelly@163.com

GUO Xinrong received her B.Sc. and M.Sc. degrees in radio science from Xidian University, Xian, China, in 2011 and 2014, respectively. She is currently a Lecturer with Armed police Engineering University, Xian, China. Her research interests include inverse synthetic aperture radar imaging and computational electromagnetics. E-mail: rapgxr@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (62101603), the Shenzhen Science and Technology Program (KQTD20190929172704911), the Aeronautical Science Foundation of China (2019200M1001), the National Nature Science Foundation of Guangdong (2021A1515011979), the Guangdong Key Laboratory of Advanced IntelliSense Technology (2019B121203006), and the Pearl River Talent Recruitment Program (2019ZT08X751).

Abstract

Abstract:

With the rapidly growing abuse of drones, monitoring and classification of birds and drones have become a crucial safety issue. With similar low radar cross sections (RCSs), velocities, and heights, drones are usually difficult to be distinguished from birds in radar measurements. In this paper, we propose to exploit different periodical motions of birds and drones from high-resolution Doppler spectrum sequences (DSSs) for classification. This paper presents an elaborate feature vector representing the periodic fluctuations of RCS and micro kinematics. Fed by the Doppler spectrum and feature sequence, the long to short-time memory (LSTM) is used to solve the time series classification. Different classification schemes to exploit the Doppler spectrum series are validated and compared by extensive real-data experiments, which confirms the effectiveness and superiorities of the proposed algorithm.

Key words: target classification, long-to-short memory (LSTM), drone discrimination, Doppler spectrum series

Jia DUAN, Lei ZHANG, Yifeng WU, Yue ZHANG, Zeya ZHAO, Xinrong GUO. Classification of birds and drones by exploiting periodical motions in Doppler spectrum series[J]. Journal of Systems Engineering and Electronics, 2023, 34(1): 19-27.

Figures/Tables 8

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References 25

1	LACHER A R, ZEITLIN A, MARONEY D R, et al. Airspace integration alternatives for unmanned aircraft. Proc. of the AUVSI’s Unmanned Systems Asia-Pacific, 2010: 10-0090.
2	YU X Y, ZHU Y, QIU L X, et al Energy efficient offloading strategy for UAV aided edge computing systems. Systems Engineering and Electronics, 2022, 44 (3): 1022- 1029.
3	ZENG Y, ZHANG R, LIM T J, Wireless communications with unmanned aerial vehicles: opportunities and challenges. IEEE Communications Magazine, 2016, 54(5): 36–42.
4	MOTLAGH N H, BAGUA M, TALEB T UAV-based IoT platform: a crowd surveillance use case. IEEE Communications Magazine, 2017, 55 (2): 128- 134. doi: 10.1109/MCOM.2017.1600587CM
5	RAHMAN S, ROBERTSON D A Classification of drones and birds using convolutional neural networks applied to radar micro-Doppler spectrogram images. IET Radar, Sonar & Navigation, 2020, 14 (5): 653- 661.
6	HUIZING A, HEILIGERS M, DEKKER B, et al Deep learning for classification of mini-UAVs using micro-Doppler spectrograms in cognitive radar. IEEE Aerospace and Electronic Systems Magazine, 2019, 34 (11): 46- 56. doi: 10.1109/MAES.2019.2933972
7	BJORKLUND S, WADSTROMER N Target detection and classification of small drones by deep learning on radar micro-Doppler. Proc. of the International Rader Conference, 2019, 19573428.
8	RITCHIE M, FIORANELLI F, BORRION H, et al Multistatic micro-Doppler radar feature extraction for classification of unloaded/loaded micro-drones. IET Radar, Sonar & Navigation, 2017, 11 (1): 116- 124.
9	KANG K B, CHOI J H, CHO B L, et al Analysis of micro-Doppler signatures of small UAVs based on Doppler spectrum. IEEE Trans. on Aerospace and Electronic Systems, 2021, 57 (5): 3252- 3267. doi: 10.1109/TAES.2021.3074208
10	LI T M Y, WEN B Y, TIAN Y W, et al Numerical simulation and experimental analysis of small drone rotor blade polarimetry based on RCS and micro-Doppler signature. IEEE Antennas and Wireless Propagation Letters, 2019, 18 (1): 187- 191. doi: 10.1109/LAWP.2018.2885373
11	GONG J K, YAN J, LI D, et al Theoretical and experimental analysis of radar micro-Doppler signature modulated by rotating blades of drones. IEEE Antennas and Wireless Propagation Letters, 2020, 19 (10): 1659- 1663. doi: 10.1109/LAWP.2020.3013012
12	TORVIK B, OLSEN K E, GRIFFITHS H Classification of birds and UAVs based on radar polarimetry. IEEE Geoscience and Remote Sensing Letters, 2016, 13 (9): 1305- 1309. doi: 10.1109/LGRS.2016.2582538
13	KIM B K, KANG H S, LEE S, et al Improved drone classification using polarimetric merged-Doppler images. IEEE Geoscience and Remote Sensing Letters, 2021, 18 (11): 1946- 1950. doi: 10.1109/LGRS.2020.3011114
14	ZHAN W J, YI J X, WAN X R, et al Track-feature-based target classification in passive radar for low-altitude airspace surveillance. IEEE Sensors Journal, 2021, 21 (8): 10017- 10028. doi: 10.1109/JSEN.2021.3057706
15	MOHAJERIN N, HISTON J, DIZAJI R, et al Feature extraction and radar track classification for detecting UAVs in civilian airspace. Proc. of the IEEE Radar Conference, 2014, 674- 679.
16	TAHA B, SHOUFAN A Machine learning-based drone detection and classification: state-of-the-art in research. IEEE Access, 2019, 7, 138669- 138682. doi: 10.1109/ACCESS.2019.2942944
17	ROHLING H Radar CFAR thresholding in clutter and multiple target situations. IEEE Trans. on Aerospace and Electronic Systems, 1983, AES-19 (4): 608- 621. doi: 10.1109/TAES.1983.309350
18	VAUGHN C R Birds and insects as radar targets: a review. Proceedings of the IEEE, 1985, 73 (2): 205- 227. doi: 10.1109/PROC.1985.13134
19	AHMED N, NATARAJAN T, RAO K R Discrete cosine transform. IEEE Trans. on Computers, 1974, C-23 (1): 90- 93. doi: 10.1109/T-C.1974.223784
20	WALLACE G K The JPEG still picture compression standard. IEEE Trans. on Consumer Electronics, 1992, 38 (1): xviii- xxxiv. doi: 10.1109/30.125072
21	HOCHREITER S, SCHMIDHUBER J Long short-term memory. Neural Computation, 1997, 9 (8): 1735- 1780. doi: 10.1162/neco.1997.9.8.1735
22	GREFF K, SRIVASTAVA R K LSTM: a search space odyssey. IEEE Trans. on Neural Networks and Learning Systems, 2017, 28 (10): 2222- 2232. doi: 10.1109/TNNLS.2016.2582924
23	GERS F A, SCHMIDHUBER J, CUMMINS F Learning to forget: continual prediction with LSTM. Neural Computation, 2000, 12 (10): 2451- 2471. doi: 10.1162/089976600300015015
24	JI R P, ZHANG C Y, LIANG L X, et al Trajectory prediction of boost-phase ballistic missile based on LSTM. Systems Engineering and Electronics, 2022, 44 (6): 1968- 1976.
25	ALTMANN M, OTT P, STACHE N C, et al Learning dynamic processes from a range-Doppler map time series with LSTM networks. Proc. of the 16th European Radar Conference, 2019, 13- 16.

Symbol	Meaning	Extraction method
$v_i $	Relative velocity	Position of maximum value
$A_i $	Amplitude	Maximum value
$N_i $	Number of distributed points	CFAR
$S_i $	Sum of amplitudes	Sum of amplitudes of $N_i$ points
$a_i $	Relative acceleration	Difference of relative velocity

[1]	Binquan LI, Xiaohui HU. Effective distributed convolutional neural network architecture for remote sensing images target classification with a pre-training approach [J]. Journal of Systems Engineering and Electronics, 2019, 30(2): 238-244.
[2]	Xufeng Zhu, Caiwen Ma, Bo Liu, and Xiaoqian Cao. Target classification using SIFT sequence scale invariants [J]. Journal of Systems Engineering and Electronics, 2012, 23(5): 633-639.
[3]	Wang Yang, Lu Jiaguo & Wu Xianliang. New algorithm of target classification in polarimetric SAR [J]. Journal of Systems Engineering and Electronics, 2008, 19(2): 273-279.

Classification of birds and drones by exploiting periodical motions in Doppler spectrum series

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