Open AccessFeature Extraction and Clustering of High Dimensional Electromagnetic Interference Signals Based on Multidimensional Scaling and SOM Network
Author(s) -
Hongyi Li,
Tianshi Xu,
Haoran Lian,
Wei Chen,
Di Zhao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2078/1/012072
Subject(s) - cluster analysis , computer science , interference (communication) , computation , convolution (computer science) , feature extraction , pattern recognition (psychology) , artificial intelligence , convolutional neural network , signal (programming language) , feature (linguistics) , data mining , scaling , algorithm , artificial neural network , mathematics , channel (broadcasting) , telecommunications , linguistics , philosophy , geometry , programming language
With the wide application of communication technology, the threat of interference is becoming more and more serious. These disturbances usually appear as higher-dimensional vectors, and the nuances between the different vectors are indistinguishable to humans. A great deal of research has been done on how to deal with this interference in an automated way. But most of the computation time in these studies was spent on the training and computation of the convolution layer. As the electromagnetic equipment tends to be a large inheritance system, the electromagnetic signal also presents higher dimensional characteristics. Then the convolutional neural network represented must use larger and more convolutional layers, so the time cost is very high. Therefore, feature extraction of the original signal is carried out before clustering. After adjusting the selection of features, the algorithm performs well on our dataset.