Identification of Shortwave Radio Communication Behavior Based on Autocorrelation Spectrogram Features

Cognitive communication behavior is becoming a research hotspot in the field of communication confrontation. In theory, the behavioral intention of noncooperating parties can be obtained by analyzing communication signals. Considering the complexity of the actual electromagnetic environment, even when the signal-to-noise ratio (SNR) is low, a certain accuracy still needs to be guaranteed. In this paper, according to five types of physical burst waveforms defined by the shortwave radio interoperability standard, a signal feature extraction method based on autocorrelation spectrogram features is proposed, and a two-input convolutional neural network (CNN) for classification is designed to improve the identification ability of shortwave communication behavior. The experimental results illustrate that the five kinds of shortwave radio communication behaviors can be accurately identified even when the noise is large. The research in this paper can directly analyze the communication behavior through physical layer signal without demodulation, which has the ability to grasp the communication behavior of the shortwave radio station in real time.