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. 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;;;
  • 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:

    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:

    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:

    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:
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (62141108), and Natural Science Foundation of Tianjin (19JCQNJC01000).


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