| Title | Radar Intra-Pulse Modulation Signal Classification Using CNN Embedding and Relation Network under Small Sample Set |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Chen, Tao, Liu, Fuyue |
| Conference Name | 2021 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST) |
| Date Published | dec |
| Keywords | CNN, modulation, Network reconnaissance, novel class, pattern classification, pubcrawl, radar imaging, Radar signal classification, Receivers, Reconnaissance, resilience, Resiliency, RN, Scalability, small sample set, Technological innovation, Training |
| Abstract | For the intra-pulse modulation classification of radar signal, traditional deep learning algorithms have poor recognition performance without numerous training samples. Meanwhile, the receiver may intercept few pulse radar signals in the real scenes of electronic reconnaissance. To solve this problem, a structure which is made up of signal pretreatment by Smooth Pseudo Wigner-Ville (SPWVD) analysis algorithm, convolution neural network (CNN) and relation network (RN) is proposed in this study. The experimental results show that its classification accuracy is 94.24% under 20 samples per class training and the signal-to-noise ratio (SNR) is -4dB. Moreover, it can classify the novel types without further updating the network. |
| DOI | 10.1109/IAECST54258.2021.9695871 |
| Citation Key | chen_radar_2021 |
Radar Intra-Pulse Modulation Signal Classification Using CNN Embedding and Relation Network under Small Sample Set