%0 Journal Article %A FENG Lei %A JIANG Lei %A XU Hua %A GOU Zezhong %T Triplet Siamese Network Modulation Recognition Algorithm Based on Network Measurement %D 2021 %R 10.3778/j.issn.1002-8331.2006-0111 %J Computer Engineering and Applications %P 135-141 %V 57 %N 19 %X

Aiming at the problem of similar recognition category confusion in small sample modulation recognition algorithms based on Siamese Neural Network, and the Triplet Siamese Network modulation recognition algorithm based on network measurement is proposed. Firstly, the original features of the input triplet set are mapped into the feature subspace through the Triplet Siamese Neural Network(TSN). Then, the features of the positive and negative samples and the reference samples are input in parallel to the Relation Network(RN) shared by two parameters, and a nonlinear measure function is learned. Finally, the prototype of each category is generated through the feature vector of each category, which is used as class feature input in the testing process. At the time, in order to reduce the influence of channel noise and signal reception error on the expression of mean-type prototypes, the Local Outlier Factor(LOF) algorithm is used to eliminate the deviation data in the category. The results conducted in the public modulation data set DeepSig indicate that the algorithm model of TSN-RN-LOF makes full use of the difference information between similar categories, extracts more recognizable features and achieves better recognition performance.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2006-0111