@article{ZHANG2024, 
author = {Shunsheng ZHANG and Huancheng DING and Wenqin WANG},
title = {Multi-scale feature extraction and feature selection network for radiation source identification},
year = {2024},
journal = {Journal of National University of Defense Technology},
volume = {46},
number = {6},
pages = {141-148},
keywords = {radiation source identification, IQ signal, multi-scale feature extraction, feature selection},
url = {https://www.sciopen.com/article/10.11887/j.cn.202406015},
doi = {10.11887/j.cn.202406015},
abstract = {Convolutional neural networks currently applied to radiation source identification process the time-series IQ (in-phase and quadrature-phase) signals in two ways: one way transforms them into images, and the other way extracts shallow features of the IQ time-series data. The former way leads to a large computational effort of the algorithm, while the latter way leads to a low accuracy of the recognition rate. To address the above problems, a multi-scale feature extraction and feature selection network was proposed. After inputting the IQ signal, the shallow and multi-scale features of the IQ signal were extracted by the multi-scale feature extraction network. Then the data dimension of multi-scale features was reduced by the feature selection network. Feature enhancement was achieved by the adaptive linear rectification unit, and a single fully connected layer was used to classify the radiation source. Comparison experiments with ORACLE, CNN-DLRF and IQCNet on the FIT/CorteXlab radio frequency fingerprint recognition dataset show that the proposed network improves the recognition accuracy and reduces the computational effort to some extent.}
}