Small sample condition of communication radio signal caused poor individual recognition on radios.To solve this problem

a method about communication radio individual recognition based on semi-supervised rectangular network was proposed innovatively.Firstly

the square integral bispectrum feature was extracted from radio signal and then was corrupted by Gaussian noise.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 close to the clean input.Then the essential feature extracted was referred as the individual feature of radio signals.Individual recognition was finally accomplished by a softmax classifier.The experiment results on several radio datasets collected in actual environment indicated that the method had superior performance on identifying radio individuals with the same types under small sample condition.