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Proceedings Paper

Radio individual identification based on semi-supervised rectangular network
Author(s): Yingke Lei
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Paper 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.

Paper Details

Date Published: 17 April 2019
PDF: 14 pages
Proc. SPIE 11071, Tenth International Conference on Signal Processing Systems, 110710Z (17 April 2019); doi: 10.1117/12.2516756
Show Author Affiliations
Yingke Lei, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 11071:
Tenth International Conference on Signal Processing Systems
Kezhi Mao; Xudong Jiang, Editor(s)

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