Emitter Individual Identification Based on Sample Enhancement
The feature extraction and classification of signals based on deep learning is a current research focus,but in engineering practice,for non-cooperative signals,the sample acquisition process is extremely complicated,which results that the effective sample is rare. Therefore,a fast original signal sample enhancement method is pro-posed to improve the idividual recognition rate. Firstly,the original signal samples are processed by noise addition,segmentation and recombination,and frequency-shift transformation to obtain the enhanced sample set. Then,the stack self-coding network and convolutional neural network are used to test the enhanced sample set respectively. The test results show that in the case of small samples,after sample enhancement processing,the accuracy of individual radiation source identification has been significantly improved.
NETL
NSTL
万方数据
