1. A Radar Signal Recognition Approach via IIF-Net Deep Learning Models
- Author
-
Wei Wang, Ji Li, Jianping Ou, and Huiqiang Zhang
- Subjects
Article Subject ,General Computer Science ,Orthogonal frequency-division multiplexing ,Computer science ,020209 energy ,General Mathematics ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Chaotic ,Neurosciences. Biological psychiatry. Neuropsychiatry ,02 engineering and technology ,Signal-To-Noise Ratio ,Convolutional neural network ,law.invention ,Deep Learning ,law ,0202 electrical engineering, electronic engineering, information engineering ,Radar ,Universal Software Radio Peripheral ,business.industry ,General Neuroscience ,Deep learning ,Pattern recognition ,General Medicine ,Software-defined radio ,Electronic countermeasure ,020201 artificial intelligence & image processing ,Neural Networks, Computer ,Artificial intelligence ,business ,Software ,RC321-571 ,Research Article - Abstract
In the increasingly complex electromagnetic environment of modern battlefields, how to quickly and accurately identify radar signals is a hotspot in the field of electronic countermeasures. In this paper, USRP N210, USRP-LW N210, and other general software radio peripherals are used to simulate the transmitting and receiving process of radar signals, and a total of 8 radar signals, namely, Barker, Frank, chaotic, P1, P2, P3, P4, and OFDM, are produced. The signal obtains time-frequency images (TFIs) through the Choi–Williams distribution function (CWD). According to the characteristics of the radar signal TFI, a global feature balance extraction module (GFBE) is designed. Then, a new IIF-Net convolutional neural network with fewer network parameters and less computation cost has been proposed. The signal-to-noise ratio (SNR) range is −10 to 6 dB in the experiments. The experiments show that when the SNR is higher than −2 dB, the signal recognition rate of IIF-Net is as high as 99.74%, and the signal recognition accuracy is still 92.36% when the SNR is −10 dB. Compared with other methods, IIF-Net has higher recognition rate and better robustness under low SNR.
- Published
- 2020