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.
Highlights
Radar signal recognition is a key technology in the field of radar electronic countermeasures
Dataset. e dataset is generated by USRP N210, USRPLW N210 simulating the process of real radar signal transmission and reception. e generated signal is transformed by Choi–Williams distribution function (CWD) to obtain time-frequency images (TFIs)
In order to make the radar signal recognition more authentic and simulate the interference of a complex external environment, noises with an signal-to-noise ratio (SNR) of −10∼6 dB are added to the signal. e real radar signal transmission and reception process is simulated by USRP N210 and USRP-LW N210. e generated signals are transformed by CWD to obtain TFI for radar signal
Summary
Radar signal recognition is a key technology in the field of radar electronic countermeasures. When receiving a radar signal, it is crucial to demodulate the signal to obtain useful information, and how to identify the signal type is the key. E accuracy of signal recognition in a complex electromagnetic environment determines the pros and cons of electronic reconnaissance systems. Due to the emergence of complex electromagnetic environments and various new system radars in modern warfare, electronic reconnaissance and electronic countermeasure systems have brought serious challenges. How to identify the type of radar signal more quickly and accurately is the key and difficult point of radar signal recognition technology. Li and Ying [1] achieved the purpose of identifying and classifying radar signals by extracting different entropy features.
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