Abstract
Radar signal classification is the key link in electronic information warfare, but as radar modulation becomes more sophisticated and the electromagnetic environment of the battlefield becomes complex, it is increasingly difficult to classify the radar signal. Aiming at the problem of low accuracy of radar signal classification in a low signal-to-noise ratio environment, a classification method based on bispectrum feature and convolutional neural network is proposed, it increases the accuracy of signal classification by taking advantage of bispectrum, which suppresses the Gaussian noise and retains the phase information. Therefore, the images of the signal bispectrum after pre-processing and data enhancement can train convolutional neural networks to obtain deeper signal features. Experimental results show that radar signal classification based on bispectrum features and convolutional neural networks can effectively improve the effect of radar signal classification.
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Liu, H., Zhou, Z., Li, B., Zhu, J., Jing, X., Li, B. (2022). Radar Signal Classification Based on Bispectrum Feature and Convolutional Neural Network. In: Sun, S., Hong, T., Yu, P., Zou, J. (eds) Signal and Information Processing, Networking and Computers. ICSINC 2021. Lecture Notes in Electrical Engineering, vol 895. Springer, Singapore. https://doi.org/10.1007/978-981-19-4775-9_34
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DOI: https://doi.org/10.1007/978-981-19-4775-9_34
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