Using LeNet-5 and AlexNet Architectures in Deep Learning Approach to Detect and Classify LPI Radar Signals

Document Type : Original Article

Authors

1 Student at Islamic Azad University

2 Assistant Prof.

3 Prof. at Sharif University of Technology

Abstract

Low probability of intercept (LPI) radars are difficult to detect and identify by electronic intelligence receivers due to their low power, wide bandwidth and frequency variability. With the emergence of this technology, new methods of signal and image processing are constantly required to first identify, then classify, and finally extract the characteristics of these radar signals. To solve the problem, today deep learning is an important technical method in the signal and image processing fields. Through using this method, this paper will investigate the possibility of detecting and classifying different signals of LPI radars. To do this, using
Short-Time Fourier Transform (STFT), we will analyze the received signal in the
time-frequency domain, and then to detect and classify the LPI radar signal waveforms we send the output, in image format, to the AlexNet and the LeNet deep convolutional neural network (CNN) models. The simulation results show that, in SNR=-5dB, the accuracy of the AlexNet and the LeNet methods are 97.34% and 94% respectively, indicating the better performance of the AlexNet method.

Keywords

References

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Radar
Volume 7, Issue 1 - Serial Number 21
December 2019
Pages 117-128

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History

  • Receive Date: 03 May 2019
  • Revise Date: 31 December 2019
  • Accept Date: 19 January 2020
  • Publish Date: 23 August 2019

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