Radio individual identification based on semi-supervised rectangular network

Yingke Lei

doi:10.1117/12.2516756

17 April 2019 Radio individual identification based on semi-supervised rectangular network

Yingke Lei

Proceedings Volume 11071, Tenth International Conference on Signal Processing Systems; 110710Z (2019) https://doi.org/10.1117/12.2516756
Event: Tenth International Conference on Signal Processing Systems, 2018, Singapore, Singapore

Abstract

Small sample condition of communication radio signal caused the poorness of individual recognition on radios. To solve this problem, a method about communication radio individual identification based on semi-supervised rectangular network was proposed innovatively. Firstly, the square integral bispectrum feature was extracted from radio signal and then was artificially injected Gaussian noise to be corrupted. The corrupted sample was passed to the encoder of semi-supervised rectangular network for supervised training. The trained parameterization was then mirrored to decoder through the lateral connection across the model. And the output was forced by decoder through unsupervised learning to be closely to the clean input. While the optimal parameters was obtained by minimizing cost function of full network, the essential feature extracted was referred as the individual feature of radio signals. Individual recognition was finally accomplished by a softmax classifier. The robustness of the method proposed was verified on several radio datasets collected in actual environment. And experiment results indicated that the method has superior performance on identifying radio individuals with the same types under small sample condition.

Citation Download Citation

Yingke Lei "Radio individual identification based on semi-supervised rectangular network", Proc. SPIE 11071, Tenth International Conference on Signal Processing Systems, 110710Z (17 April 2019); https://doi.org/10.1117/12.2516756

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available