Quasi-LFM radar waveform recognition based on fractional Fourier transform and time-frequency analysis

doi:10.23919/JSEE.2021.000097

Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (5): 1130-1142.doi: 10.23919/JSEE.2021.000097

• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

Quasi-LFM radar waveform recognition based on fractional Fourier transform and time-frequency analysis

Cunxiang XIE¹(), Limin ZHANG¹(), Zhaogen ZHONG^2,*()

¹ Department of Information Fusion, Naval Aviation University, Yantai 264001, China
² School of Aviation Basis, Naval Aviation University, Yantai 264001, China

Received:2020-12-16 Online:2021-10-18 Published:2021-11-04
Contact: Zhaogen ZHONG E-mail:932304145@qq.com;iamzlm@163.com;zhongzhaogen@163.com
About author:|XIE Cunxiang was born in 1996. He received his B.S. degree in communication engineering from the Naval Aviation University, in 2019. He is currently pursuing his M.S. degree in information and communication engineering with the Department of Information Fusion, Naval Aviation University. His research interests include deep learning and specific emitter identification. E-mail: 932304145@qq.com||ZHANG Limin was born in 1966. He received his Ph.D. degree in signal processing technology from Tianjin University, in 2005. Since 2005, he has been a professor with the Naval Aviation University. His research interests include satellite communication signal processing. E-mail: iamzlm@163.com||ZHONG Zhaogen was born in 1984. He received his Ph.D. degree in information and communication engineering from the Naval Aviation University, in 2013. He is currently an associate professor with the Naval Aviation University. His research interests include spread spectrum signal processing. E-mail: zhongzhaogen@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (91538201), the Taishan Scholar Project of Shandong Province (ts201511020), and the project supported by Chinese National Key Laboratory of Science and Technology on Information System Security (6142111190404).

Abstract

Abstract:

Recent advances in electronics have increased the complexity of radar signal modulation. The quasi-linear frequency modulation (quasi-LFM) radar waveforms (LFM, Frank code, P1?P4 code) have similar time-frequency distributions, and it is difficult to identify such signals using traditional time-frequency analysis methods. To solve this problem, this paper proposes an algorithm for automatic recognition of quasi-LFM radar waveforms based on fractional Fourier transform and time-frequency analysis. First of all, fractional Fourier transform and the Wigner-Ville distribution (WVD) are used to determine the number of main ridgelines and the tilt angle of the target component in WVD. Next, the standard deviation of the target component's width in the signal's WVD is calculated. Finally, an assembled classifier using neural network is built to recognize different waveforms by automatically combining the three features. Simulation results show that the overall recognition rate of the proposed algorithm reaches 94.17% under 0 dB. When the training data set and the test data set are mixed with noise, the recognition rate reaches 89.93%. The best recognition accuracy is achieved when the size of the training set is taken as 400. The algorithm complexity can meet the requirements of real-time recognition.

Key words: quasi-linear frequency modulation (quasi-LFM) radar waveform, time-frequency distribution, fractional Fourier transform (FrFT), assembled classifier

Cunxiang XIE, Limin ZHANG, Zhaogen ZHONG. Quasi-LFM radar waveform recognition based on fractional Fourier transform and time-frequency analysis[J]. Journal of Systems Engineering and Electronics, 2021, 32(5): 1130-1142.

Figures/Tables 15

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Code type	Definition of ${\phi _i}$
LFM	$\phi = {\text{π}}\mu {t^2}$
Frank	${\phi _{mn}} = 2{\text{π}}\left( {m - 1} \right)\left( {n - 1} \right)/M$
P1	${\phi _{mn}} = - {\text{π}}\left[ {M - \left( {2n - 1} \right)} \right]\left[ {\left( {n - 1} \right)M + \left( {m - 1} \right)} \right]/M$
P2	${\phi _{mn} } = - {\text{π} }\left( {2n - 1 - M} \right)\left( {2m - 1 - M} \right)/ {2M}$
P3	${\phi _k} = {\text{π}}{\left( {k - 1} \right)^2}/{N_c}$
P4	${\phi _k} = {\text{π}}{\left( {k - 1} \right)^2}/{N_c} - {\text{π}}\left( {k - 1} \right)$

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Quasi-LFM radar waveform recognition based on fractional Fourier transform and time-frequency analysis

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