Optical Feature Extraction Via The Radon Transform

1984 ◽  
Vol 23 (5) ◽  
Author(s):  
Gene R. Gindi ◽  
Arthur F. Gmitro
1996 ◽  
Vol 21 (19) ◽  
pp. 1612 ◽  
Author(s):  
Ju-Seog Jang ◽  
Dong-Hak Shin

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Yipeng Zhou ◽  
Xing Wang ◽  
You Chen ◽  
Yuanrong Tian

Specific emitter identification is a technique that distinguishes different emitters using radio fingerprints. Feature extraction and classifier selection are critical factors affecting SEI performance. In this paper, we propose an SEI method using the Bispectrum-Radon transform (BRT) and a hybrid deep model. We propose BRT to characterize the unintentional modulation of pulses due to the superiority of bispectrum distributions in characterizing nonlinear features of signals. We then apply a hybrid deep model based on denoising autoencoders and a deep belief network to perform further deep feature extraction and discriminative identification. We design an automatic dependent surveillance-broadcast signal acquisition system to capture signals and to build dataset for validating our proposed SEI method. Theoretical analysis and experimental results show that the BRT feature outperformed traditional features in characterizing UMOP, and our proposed SEI method outperformed other feature and classifier combination methods.


2013 ◽  
Vol 46 (10) ◽  
pp. 2622-2633 ◽  
Author(s):  
Anis Farihan Mat Raffei ◽  
Hishammuddin Asmuni ◽  
Rohayanti Hassan ◽  
Razib M. Othman

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