Abstract:

Aiming at the low recognition rate of radar signals under the condition of low signal-to-noise ratio, and the classification network has the limitation of identifying the newly added signal types in the sample library, a radar signal recognition method based on the deep residual shrinking attention network is proposed. The one-dimensional radar signal is mapped to a 32-dimensional vector space through the network. The residual connection in the network can effectively strengthen the dissemination ability of features and solve the problem that the network is too deep to be trained; the introduction of the attention mechanism not only constructs the mask branch to act as the feature selector of the main branch, but also helps the network adaptively select the appropriate threshold for soft thresholding, so as to reduce the influence of noise or redundant information in the network and improve the robustness of the network to noise. During the training process, ranked list loss (RLL) and the classification loss function jointly guide the network training. RLL can effectively overcome the problem of traditional metric learning loss function ignoring features within the class, and the classification loss function can make up for the problem of insensitivity to the overall distribution of the sample under metric loss optimization. Experiments show that this method can not only improve the recognition accuracy of low-signal-to-noise ratio radar signals, but also has the ability to identify newly added signal types in the sample library.

Key words: radar signal recognition, deep residuals shrink attention network, soft thresholding, attention mechanism, loss function