Radar emitter recognition based on CNN and LSTM

2021 ◽  
Author(s):  
Han Liu ◽  
Donghang Cheng ◽  
Xiaojun Sun ◽  
Feng Wang
Keyword(s):  
Emitter Recognition

scholarly journals Radar Specific Emitter Recognition Based on DBN Feature Extraction

2019 ◽  
Vol 1176 ◽  
pp. 032025 ◽  
Author(s):  
Xiaoxuan Dong ◽  
Siyi Cheng ◽  
Jinheng Yang ◽  
Yipeng Zhou
Keyword(s):  
Feature Extraction ◽  
Emitter Recognition

A novel RBF neural network for radar emitter recognition based on Rough Sets

2012 ◽  
Vol 35 (7) ◽  
pp. 901-907 ◽  
Author(s):  
Zhilu Wu ◽  
Zhutian Yang ◽  
Zhendong Yin ◽  
Lihua Zuo ◽  
Hansong Gao
Keyword(s):  
Neural Network ◽  
Rough Sets ◽  
Rbf Neural Network ◽  
Emitter Recognition

scholarly journals Radar Signal Emitter Recognition Based on Combined Ensemble Empirical Mode Decomposition and the Generalized S-Transform

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Bin Liu ◽  
Youqian Feng ◽  
Zhonghai Yin ◽  
Xiangyu Fan
Keyword(s):  
Empirical Mode Decomposition ◽  
Prior Information ◽  
Ensemble Empirical Mode Decomposition ◽  
Radar Signal ◽  
Time Frequency ◽  
Low Snr ◽  
Mode Decomposition ◽  
S Transform ◽  
Emitter Recognition ◽  
Generalized S Transform

Present radar signal emitter recognition approaches suffer from a dependency on prior information. Moreover, modern emitter recognition must meet the challenges associated with low probability of intercept technology and other obscuration methodologies based on complex signal modulation and must simultaneously provide a relatively strong ability for extracting weak signals under low SNR values. Therefore, the present article proposes an emitter recognition approach that combines ensemble empirical mode decomposition (EEMD) with the generalized S-transform (GST) for promoting enhanced recognition ability for radar signals with complex modulation under low signal-to-noise ratios in the absence of prior information. The results of Monte Carlo simulations conducted using various mixed signals with additive Gaussian white noise are reported. The results verify that EEMD suppresses the occurrence of mode mixing commonly observed using standard empirical mode decomposition. In addition, EEMD is shown to extract meaningful signal features even under low SNR values, which demonstrates its ability to suppress noise. Finally, EEMD-GST is demonstrated to provide an obviously better time-frequency focusing property than that of either the standard S-transform or the short-time Fourier transform.

scholarly journals Radar Emitter Recognition Based on PSO-BP Network

2012 ◽  
Vol 1 ◽  
pp. 213-219 ◽  
Author(s):  
Yu Zhi-fu ◽  
Li Jun-wu ◽  
Liu Kai
Keyword(s):  
Bp Network ◽  
Emitter Recognition
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