Paper Info
Title
A Radar Signal Recognition System Based On Non-Negative Matrix Factorization Network And Improved Artificial Bee Colony Algorithm
Abstract
The development of cognitive radio and electronic warfare brings new challenges to radar electronic reconnaissance, the recognition of radar signal plays an extreme important role in radar electronic reconnaissance. In order to realize the reliable recognition of radar signal at the condition of low signalto-noise ratio (SNR), we propose a new radar signal recognition system based on non-negative matrix factorization network (NMFN) and ensemble learning, which can recognize radar signals including BPSK, LFM, NLFM, COSTAS, FRANK, P1, P2, P3 and P4. First, we explore feature extractor based on convolutional neural network (CNN), which applies transfer learning to solve the problem of small sample size. Second, we propose non-negative matrix factorization network to extract features, which can reduce the redundant information. Third, we develop feature fusion algorithm based on stacked autoencoder (SAE), which can acquire essential expression of features and reduce dimension of features. Finally, we propose improved artificial bee colony algorithm (IABC) as the strategy of ensemble learning, which can improve the recognition rate. The simulation results show that the recognition rates reach 94.23% at -4 dB, 99.82% at 6 dB.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2936669
IEEE ACCESS
Keywords
DocType
Volume
Radar signal recognition, non-negative matrix factorization network, transfer learning, feature fusion, improved artificial bee colony algorithm
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Jingpeng Gao101.01
Yi Lu200.34
Junwei Qi311.05
Liangxi Shen400.68