research-article

Low Probability of Intercept Radar Signal Recognition Based on the Improved AlexNet Model

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Chen XinAuthors Info & Claims

ICDSP '18: Proceedings of the 2nd International Conference on Digital Signal Processing

Pages 119 - 124

https://doi.org/10.1145/3193025.3193037

Published: 25 February 2018 Publication History

Abstract

In order to solve the problem that low recognition rate and less signal type of low probability of intercept (LPI) radar signal at -6dB of low signal-to-noise ratio (SNR), the paper presents a method based on Smooth Pseudo Wigner-Ville distribution (SPWVD) for signal time-frequency analyze and an improved-AlexNet deep convolutional neural network (DCNN) model for low probability of intercept radar signal to classification. First of all, the time-frequency images of radar signals are accessed by time-frequency analysis of SPWVD. Next, to fit for input of the model size selected later and weaken the influence of noise, time-frequency images must be denoised and clipped processing by wavelet threshold filtering and bi-cubic interpolation. After that, employing TensorFlow frame and GPU to built improved-AlexNet and that accelerate the training of model. Last but not least, The model will extract feature and classify 10 type of radar signals include that CW, LFM, NLFM, BPSK, Costas, Frank, T1, T2, T3 and T4. The simulation results show that the overall correct recognition rate(CRR) of radar signals is 92.5% at -6dB that higher than existing methods.

References

[1]

Zhang Guo-zhu, Huang Ke-sheng, Jiang, Wen-li, "Emitter Feature Extract Method Based on Signal Envelope," Journal of Systems Engineering and Electronics, vol. 294, June. 2006, pp. 795--797.

[2]

P. Misans, M. Terauds, "CW Doppler Radar Based Land Vehicle Speed Measurement Algorithm Using Zero Crossing and Least Squares Method," Electronics Conference (BEC2012), October. 2012, pp. 161--164.

[3]

Wang Xing, Zhou Yipeng, Zhou Dongqing, Chen Zhonghui, Tian Yuanrong, "Research on Low Probability of Intercept Radar Signal Recognition Using Deep Belief Network and Bispectra Diagonal Slice," Journal of Electronics & Information Technology. Vol. 38, Nov. 2016.

[4]

Xiao LeQun, Zhang YuLing, Zhao YongJun, "Radar signal feature extraction based on wavelet ridge and high order spectral analysis," IET International Radar Conference(IRC2009), Apr.2009.

[5]

Lütfiye Durak, Orhan Arıkan, "Short-time Fourier transform: Two fundamental properties and an optimal implementation," IEEE Transactions on Signal Processing, vol. 51, May. 2003, pp. 1231--1242.

Digital Library

[6]

Chang Xu, Cong Wang, Wei Liu, "Nonstationary Vibration Signal Analysis Using Wavelet-Based Time-Frequency Filter and Wigner-Ville Distribution," Journal of Vibration &Acoustics,vol.138, Jun.2016.

[7]

Glauber Ribeiro Pereiraa,Liliam Fernandes de Oliveirab, "Reducing cross terms effects in the Choi--Williams transform of mioelectric signals," Elsevier North-Holland, vol. 111, June. 2013, pp. 685--692.

Digital Library

[8]

Yibing Li, Yanhuan Wang, Yun Lin, "Recognition of Radar Signals Modulation Based on Short Time Fourier Transform and Reduced Fractional Fourier Transform," Journal of Information &Computational Science, vol. 10, Nov. 2013, pp. 5171--5178.

[9]

Jarmo Lundén, Visa Koivunen, "Automatic Radar Waveform Recognition," IEEE Journal of Selected Topics in Signal Processing, vol. 1, June. 2007, pp. 124--136.

[10]

Stanislav Pikula, Petr Beneš, "A New Method for Interference Reduction in the Smoothed Pseudo Wigner-Ville Distribution," International Conference on Sensing Technology(ICST2014), Sep.2014, pp. 599--603.

[11]

Alex Krizhevsky, Ilya Sutskever,Geoffrey E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Communications of the Acm, vol. 60, June. 2017, pp. 84--90.

Digital Library

[12]

Zheng HongHuang, He XiPing, Bin Fang, Li Xia, "Image denoising and comparison by improving threshold based on the dyadic wavelet transform," Internation Conference on Wavelet Analysis and Patter Recognition(ICWAPR2007), Jan. 2008.

[13]

Jan Matuszewski, Katarzyna Sikorska-Łukasiewicz, "Neural Network Application for Emitter Identification, "International Radar Symposium, (IRS2017), Aug. 2017.

[14]

Ahmed El-Sawy, Hazem EL-Bakry, Mohamed Loey, "CNN for handwritten arabic digits recognition based on LeNet-5," International Conference on Advanced Intelligent Systems and Informatics(AISI2016), Oct. 2016. pp. 566--575.

[15]

Zhang Ming, Lutao Liu, Guo LiMing, "Convolutional Neural Networks for AutomaticCognitive Radio Waveform Recognition," IEEE Aceess, vol. 5, June. 2017, pp. 11074--11082.

[16]

Zhang Ming, Lutao Liu, Diao Ming, "LPI Radar Waveform Recognition Based on Time-Frequency Distribution," sensor, vol. 16, Oct. 2016, pp. 1682--1686.

Cited By

Xiong JPan JDu M(2023)A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy EnvironmentsRemote Sensing10.3390/rs1516408315:16(4083)Online publication date: 19-Aug-2023
https://doi.org/10.3390/rs15164083
Liu LLi X(2021)Radar signal recognition based on triplet convolutional neural networkEURASIP Journal on Advances in Signal Processing10.1186/s13634-021-00821-82021:1Online publication date: 13-Nov-2021
https://doi.org/10.1186/s13634-021-00821-8

Index Terms

Low Probability of Intercept Radar Signal Recognition Based on the Improved AlexNet Model
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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ICDSP '18: Proceedings of the 2nd International Conference on Digital Signal Processing

February 2018

198 pages

ISBN:9781450364027

DOI:10.1145/3193025

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 February 2018

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ICDSP 2018

ICDSP 2018: 2018 2nd International Conference on Digital Signal Processing

February 25 - 27, 2018

Tokyo, Japan

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Cited By

Xiong JPan JDu M(2023)A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy EnvironmentsRemote Sensing10.3390/rs1516408315:16(4083)Online publication date: 19-Aug-2023
https://doi.org/10.3390/rs15164083
Liu LLi X(2021)Radar signal recognition based on triplet convolutional neural networkEURASIP Journal on Advances in Signal Processing10.1186/s13634-021-00821-82021:1Online publication date: 13-Nov-2021
https://doi.org/10.1186/s13634-021-00821-8

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