Frequency Modulation Recognition for Radar Signal 
based on Resnet-LSTM Network

Xuying Zhang*; Yang Liu; Xinwen Yan; Longbiao Hu; Guoyuan Chang

doi:10.18686/aitr.v2i3.4426

Frequency Modulation Recognition for Radar Signal based on Resnet-LSTM Network

Xuying Zhang*, Yang Liu, Xinwen Yan, Longbiao Hu, Guoyuan Chang

Abstract

In order to satisfy the modulation recognition requirements for radar signal with large scale changes in pulse-width and bandwidth,
the Resnet-LSTM network is proposed in this paper. First, the waveform is preprocessed by self-correlation, and then the I/Q waveform and
self-correlation result are as the network input. Secondly, the local characteristics were extracted by Resnet network; and lastly the LSTM
network is adopted to extract the serialization representation. The proposed algorithm avoids the flaw of CNN network cannot adapt to variable length, and problem of memory forgetting of the LSTM network for long sequence is reduced. The simulation shows that the modulation
recognition accuracy is more than 96.1% over the detection sensitivity for LFM, Parabolic-NLFM, Triangular-NLFM, Sine-NLFM, Sawtooth-NLFM and FSK signals.

Keywords

Large scale changes; Self-correlation; Resnet-LSTM network

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References

[1] ZHANG M, LIU L T, DIAO M. LPIradar waveform recognition based on time-frequency distribution[J].Sensors, 2016, 16(10):1682.

[2] WANG X B, HUANG G M, ZHOUZ W, etal.Radar emitter recognition based on the energy cumulant of short time Fourier transform

and reinforced deep belief network[J].Sensors, 2018, 18(9):3103.

[3] Tang, Y., & Zhang, Y. (2010). Modulation recognition based on support vector machine with kernel function. Journal of Computer Science and Technology, 25(4), 682-687.

[4] Yu Zhengyang, Tang Jianlong, Wang Zhao. GCPS: A CNN Performance Evaluation Criterion for Radar Signal Intrapulse Modulation

Recognition[J]. IEEE Communications letters, 2021, 25(7):2290-2294.

[5] A Deep Learning Framework for Modulation Recognition in Communication Signals.

[6] End-to-End Deep Learning for Modulation Recognition in Communication Systems.

[7] HERMAWAN A P, GINANJAR R R, KIM D S, et al. CNN-based automatic modulation classification for beyond 5G communications[J].

IEEE Communications Letters, 2020, 24(5): 1038-1041.

[8] AN Zeliang, ZHANG Tianqi, SHEN Ming, et al. Series-constellation feature based blind modulation recognition for beyond 5G MIMO-OFDM systems with channel fading[J]. IEEE Transactions on Cognitive Communications and Networking, 2022, 8(2):793-811.

DOI: http://dx.doi.org/10.18686/aitr.v2i3.4426

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