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- Title
Radar signal recognition method based on deep residual network and triplet loss.
- Authors
SHI Limeng; YANG Chengzhi; WU Hongchao
- Abstract
To solve the problem that the classification network is difficult to effectively expand the number of classifications, a radar signal recognition method based on deep residual network and triplet loss is proposed. This method firstly takes the radar signal as the input of the deep residual network, maps the radar signal to 128-dimensional Euclidean space through one-dimensional convolution, and obtains the signal's eigenvector; then uses the triplet loss function to adjust the network parameters so that the Euclidean distance of feature vectors between homogeneous signals decreases and the distance between different types of signals increases; finally, the classification of the signals is realized through a sample library-based recognition algorithm. Experimental results show that compared with traditional classification networks, this method ensures the accuracy of recognition while enabling the model to effectively expand the number of classifications.
- Subjects
RADAR; EUCLIDEAN distance; SIGNAL classification; MIMO radar
- Publication
Systems Engineering & Electronics, 2020, Vol 42, Issue 11, p2506
- ISSN
1001-506X
- Publication type
Article
- DOI
10.3969/j.issn.1001-506X.2020.11.12