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A Radar Signal Recognition Approach via IIF-Net Deep Learning Models
Ji Li1, Huiqiang Zhang1, Jianping Ou2
1School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China.
Computational Intelligence and Neuroscience
|September 10, 2020
Summary
This study introduces IIF-Net, a novel convolutional neural network for identifying radar signals in complex electromagnetic environments. IIF-Net achieves high accuracy, even at low signal-to-noise ratios (SNRs), offering robust electronic countermeasures.
Area of Science:
- Electronic warfare and signal processing.
- Artificial intelligence in defense applications.
Background:
- Modern battlefields present a complex electromagnetic environment, necessitating rapid and accurate radar signal identification for electronic countermeasures.
- Existing methods may struggle with accuracy and robustness in low signal-to-noise ratio (SNR) conditions.
Purpose of the Study:
- To develop an efficient and accurate deep learning model for radar signal recognition.
- To enhance the robustness of radar signal identification systems under challenging SNR conditions.
Main Methods:
- Simulation of eight distinct radar signals (Barker, Frank, chaotic, P1-P4, OFDM) using software-defined radio peripherals (USRP N210, USRP-LW N210).
- Generation of time-frequency images (TFIs) for each signal using the Choi-Williams distribution function (CWD).
- Design of a global feature balance extraction module (GFBE) and a novel convolutional neural network, IIF-Net, for signal classification.
Main Results:
- IIF-Net achieved a recognition rate of 99.74% at SNRs above -2 dB.
- The model maintained 92.36% accuracy even at an SNR of -10 dB.
- IIF-Net demonstrated superior recognition rates and robustness compared to other methods, particularly in low SNR environments.
Conclusions:
- The proposed IIF-Net offers a computationally efficient and highly accurate solution for radar signal identification.
- IIF-Net exhibits significant robustness in low SNR conditions, making it suitable for electronic countermeasures in complex electromagnetic environments.
