Micro-Doppler feature extraction of micro-rotor UAV under the background of low SNR

doi:10.23919/JSEE.2022.000138

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (6): 1127-1139.doi: 10.23919/JSEE.2022.000138

• ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

Micro-Doppler feature extraction of micro-rotor UAV under the background of low SNR

Weikun HE¹(), Jingbo SUN²^,*(), Xinyun ZHANG¹(), Zhenming LIU¹()

¹ College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
² Cyber Intelligent Technology Co., Ltd, Ji’nan 250100, China

Received:2021-06-30 Online:2022-12-24 Published:2022-12-24
Contact: Jingbo SUN E-mail:hwkcauc@126.com;dllcauc@126.com;xyzhangcauc@126.com;1755979259@qq.com
About author:
HE Weikun was born in 1977. She received her Ph.D. degree from Tianjin University in 2012. From 2010 to 2011, she worked at the Aeronautics and Space Institute of Toulouse, France as a distinguished research scholar. She is currently a professor in the Tianjin Key Laboratory for Advance Signal Processing, Civil Aviation University of China. Her research interests include radar signal processing, and wind farm clutter suppression. E-mail: hwkcauc@126.com

SUN Jingbo was born in 1996. He received his B.S. degree in electronic information science and technology from Heze University in 2018 and his M.S. degree in electronic and communication engineering from Civil Aviation University of China in 2021. He is an engineer from Cyber Intelligent Technology Co., Ltd in Shandong, China. His current research interests mainly focus on micro-Doppler feature analysis and extraction of micro-rotor UAV. E-mail: dllcauc@126.com

ZHANG Xinyun was born in 1996. She is currently pursuing her M.S. degree with the College of Electronic Information and Automation, Civil Aviation University of China. Her current research interests mainly include radar echoes modeling and micro-motion feature analysis for the bird flock. E-mail: xyzhangcauc@126.com

LIU Zhenming was born in 1996. He is currently pursuing his M.S. degree with the College of Electronic Information and Automation, Civil Aviation University of China. His current research interest is mainly the identification of non-cooperative targets under the background of strong dynamic clutter. E-mail: 1755979259@qq.com
Supported by:
This work was supported by the National Natural Science Foundation of China (62141108), and Natural Science Foundation of Tianjin (19JCQNJC01000).

Abstract

Abstract:

Micro-Doppler feature extraction of unmanned aerial vehicles (UAVs) is important for their identification and classification. Noise and the motion state of the UAV are the main factors that may affect feature extraction and estimation precision of the micro-motion parameters. The spectrum of UAV echoes is reconstructed to strengthen the micro-motion feature and reduce the influence of the noise on the condition of low signal to noise ratio (SNR). Then considering the rotor rate variance of UAV in the complex motion state, the cepstrum method is improved to extract the rotation rate of the UAV, and the blade length can be intensively estimated. The experiment results for the simulation data and measured data show that the reconstruction of the spectrum for the UAV echoes is helpful and the relative mean square root error of the rotating speed and blade length estimated by the proposed method can be improved. However, the computation complexity is higher and the heavier computation burden is required.

Key words: micro-rotor unmanned aerial vehicle (UAV), low signal to noise ratio (SNR), micro-Doppler, feature extraction, parameter estimation

Weikun HE, Jingbo SUN, Xinyun ZHANG, Zhenming LIU. Micro-Doppler feature extraction of micro-rotor UAV under the background of low SNR[J]. Journal of Systems Engineering and Electronics, 2022, 33(6): 1127-1139.

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References 30

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Parameter	Value
Frequency/GHz	5.5
Pulse repetition interval/µs	60
Sampling rate/MHz	80
Bandwidth/MHz	20
Time window/ms	192

Parameter	Quadcopter
Number of rotors	4
Number of blades	2
Blade length/mm	120
Rotating rate/(r/s)	Motion state 50,64
Fuselage speed/(m/s)	6

Micro-motion parameter	Theoretical value	Estimated value		Relative mean square root error
Micro-motion parameter	Theoretical value	Before reconstruction	After reconstruction	Before reconstruction	After reconstruction
Maximum Doppler frequency	1 548.22 Hz	1652.01 Hz	1516.30 Hz	4.17%	1.73%
Rotating speed	56 r/s	57.13 r/s	56.03 r/s	1.65%	0.21%
Blade length	120 mm	123.93 mm	119.23 mm	4.83%	2.21%

Micro-motion parameter	Theoretical value	Estimated value		Relative mean square root error
Micro-motion parameter	Theoretical value	Before reconstruction	After reconstruction	Before reconstruction	After reconstruction
Rotating speed 1	50 r/s	50.83 r/s	49.97 r/s	0.69%	0.45%
Maximum Doppler frequency corresponding to rotation speed 1	1350.29 Hz	1452.50 Hz	1308 Hz	5.35%	3.51%
Blade length corresponding to rotation speed 1	120 mm	125.91 mm	119.11 mm	5.30%	3.51%
Rotating speed 2	64 r/s	65.01 r/s	64.08 r/s	0.93%	0.33%
Maximum Doppler frequency corresponding to rotation speed 2	1728.37 Hz	1821.60 Hz	1683.23 Hz	4.49%	2.83%
Blade length corresponding to rotation speed 2	120 mm	126.21 mm	118.50 mm	4.55%	2.89%

Calculation time	Maximum Doppler frequency	Rotation speed
Before spectrum reconstruction	22.01	3.56
After spectrum reconstruction	25.74	5.77

Micro-Doppler feature extraction of micro-rotor UAV under the background of low SNR

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