Multi-rotor UAV's micro-Doppler characteristic analysis and feature extraction

doi:10.7523/j.issn.2095-6134.2019.02.011

Abstract

Abstract: In order to solve the problem of multi-rotor UAV target recognition, a micro-Doppler feature extraction algorithm based on Gabor transform is proposed by combining the instantaneous frequency estimation and fast Fourier transform(FFT). Firstly, the echo model of multi-rotor UAV is established, and the analysis of the influences of number of blades, rotor speed, and initial phase on the micro-Doppler characteristics is made by simulation. Then, Gabor transform is adopted to obtain time-frequency feature. Based on this, micro-Doppler frequency is extracted by the instantaneous frequency maximum value method, and the FFT of instantaneous frequency is used to extract rotor number and rotational frequency. With the above information, the length of the blade is calculated. Finally, the measured data for "Phantom 3S" show that the proposed algorithm extracts the micro-Doppler parameters accurately, indicating the effectiveness of the algorithm.

Key words: target recognition, UAV rotor, time-frequency analysis, micro-Doppler, parameter estimation

CLC Number:

TN957.51

MA Jiao, DONG Yongwei, LI Yuan, LI Lingxiao, YANG Jiefang. Multi-rotor UAV's micro-Doppler characteristic analysis and feature extraction[J]. , 2019, 36(2): 235-243.

References

[1] Hoffmann F, Ritchie M, Fioranelli F, et al. Micro-Doppler based detection and tracking of UAVs with multistatic radar[C]//IEEE Radar Conference, 2016:1-6.
[2] Chen V C, Li F, Ho S S, et al. Micro-Doppler effect in radar:phenomenon, model, and simulation study[J]. IEEE Transactions on Aerospace & Electronic Systems, 2006, 42(1):2-21.
[3] Jian M, Lu Z, Chen V C. Experimental study on radar micro-Doppler signatures of unmanned aerial vehicles[C]//Radar Conference, 2017:854-857.
[4] Wit J J M D,Harmanny R I A, Premel-Cabic G. Micro-Doppler analysis of small UAVs[C]//European Radar Conference, 2012:210-213.
[5] Nanzer J A, Chen V C. Microwave interferometric and Doppler radar measurements of a UAV[C]//Radar Conference, 2017:1628-1633.
[6] Robertson D A. Millimeter-wave micro-Doppler measurements of small UAVs[C]//SPIE Defense+Security, 2017:101880T.
[7] Govoni M A. Micro-Doppler signal decomposition of small commercial drones[C]//Radar Conference, 2017:425-429.
[8] Harmanny R I A, Wit J J M D, Cabic G P. Radar micro-Doppler feature extraction using the spectrogram and the cepstrogram[C]//European Radar Conference, 2014:165-168.
[9] Schröder A, Murk A, Aulenbacher U, et al. Numerical RCS and micro-Doppler analysis of a consumer UAV[C]//SPIE Security+Defence, 2016:999704.
[10] Fuhrmann L, Biallawons O, Klare J, et al. Micro-Doppler analysis and classification of UAVs at Ka band[C]//Radar Symposium (IRS), 201718th International, 2017:1-9.
[11] Chen V C. The micro-Doppler effect in radar[M]. Artech House, 2011:110-112.
[12] 陈永彬, 李少东, 杨军,等. 旋翼叶片回波建模与闪烁现象机理分析[J]. 物理学报, 2016, 65(13):281-291.
[13] 陈永彬, 李少东, 陈文峰,等. 直升机旋翼叶片回波建模与特性分析[J]. 空军预警学院学报, 2015, 29(5):322-327.
[14] 陈小龙, 关键, 于晓涵,等. 基于短时稀疏时频分布的雷达目标微动特征提取及检测方法[J]. 电子与信息学报, 2017, 39(5):1017-1023.
[15] 张义安, 张冬晨, 陈卫东,等. 基于局域波分解的机动目标ISAR成像[J]. 中国科学院研究生院学报, 2009, 26(4):483-489.
[16] 陈行勇,刘永祥,姜卫东等. 雷达目标微动分辨[J]. 系统工程与电子技术,2007, 3(29):361-364.
[17] 黄志星. 基于微多普勒的空间多目标特征提取与参数估计[D]. 成都:电子科技大学, 2014.
[18] 刘阳. 微型四旋翼无人飞行器的自主起降与安全飞行研究[D]. 北京:华北电力大学, 2015.