KEYWORDS: Feature extraction, Data communications, Data modeling, Education and training, Data acquisition, Matrices, Neural networks, Receivers, Statistical modeling, Wavelet transforms
Aiming at the problem that features of communication emitter data without label information are different to extract and the classification acquisition is not high, this paper cites the contrastive learning theory, constructs a residual network with two parameters sharing as the backbone network, conducts contrastive learning on the rectangular integral bispectral features of signal augmented samples, and further extracts the feature presentation with more differentiation. To this end, the feature separability between samples from different emitters is enhanced. Then, the new features extracted are used for contrastive learning at the cluster level to complete the tasks of classification and identification. Compared with other unsupervised learning algorithms, the proposed method achieves a better identification accuracy of about 78% through experiences on the dataset of measured ultra-short wave communication stations.
By identifying the working mode of the communication radiation source, its behavioral intent can be comprehended. Tactical data link is a kind of classic communication radiation source with a variety of working modes. Aiming at the typical data link TADIL A, a method for extracting one-dimensional time-frequency features of signals using short-time Fourier transform (STFT) is proposed. Then, one-dimensional Convolutional Neural Network (CNN) DenseNet-1D is used for training and testing to complete the task of identifying different working modes. The experimental results illustrate that the working mode recognition based on the physical layer signal is feasible.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.