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Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy

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Radar Signal Modulation Recognition Based on Sep-ResNet.

Yongjiang Mao^1,2,3, Wenjuan Ren^1,2, Zhanpeng Yang^1,2

¹Key Laboratory of Network Information System Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China.

Sensors (Basel, Switzerland)

|November 27, 2021

Summary

This summary is machine-generated.

This study introduces an intelligent method using time-frequency analysis and deep neural networks to accurately identify complex radar signals, even in noisy environments. The approach achieves high recognition accuracy, demonstrating its effectiveness for radar modulation signal identification.

Keywords:

channel-separable ResNet complex Morlet wavelet image enhancement radar modulation signal time–frequency analysis

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Area of Science:

Electrical Engineering
Signal Processing
Artificial Intelligence

Background:

Space-based radar systems face challenges with signal aliasing and electronic interference, complicating signal identification.
Complex electromagnetic signals in space, especially in low signal-to-noise ratio (SNR) environments, hinder accurate radar modulation recognition.

Purpose of the Study:

To propose an intelligent recognition method for identifying radar modulation signals in low SNR environments.
To enhance the accuracy of radar signal identification amidst complex interference and noise.

Main Methods:

Utilized complex Morlet wavelet transform (CMWT) for time-frequency (T-F) analysis to extract signal characteristics and generate T-F images.
Employed adaptive filtering and morphological processing for T-F image enhancement, reducing noise interference.
Implemented a deep neural network, specifically a channel-separable ResNet (Sep-ResNet), for classifying the enhanced T-F images.

Main Results:

Achieved high-accuracy intelligent recognition of radar-modulated signals in low SNR conditions.
Demonstrated a probability of successful recognition (PSR) of 93.44% at an SNR of -10 dB.

Conclusions:

The proposed method effectively combines T-F analysis and deep learning for robust radar signal identification.
The approach shows significant promise for applications requiring accurate radar modulation recognition in challenging, low-SNR scenarios.