Abstract
This work improves a LeNet model algorithm based on a signal’s bispectral features to recognize the communication behaviors of a non-collaborative short-wave radio station. At first, the mapping relationships between the burst waveforms and the communication behaviors of a radio station are analyzed. Then, bispectral features of simulated behavior signals are obtained as the input of the network. With regard to the recognition neural network, the structure of LeNet and the size of the convolutional kernel in LeNet are optimized. Finally, the five types of communication behavior are recognized by using the improved bispectral estimation matrix of signals and the ameliorated LeNet. The experimental results show that when the signal-to-noise ratio (SNR) values are 8, 10, or 15 dB, the recognition accuracy values of the improved algorithm reach 81.5%, 94.5%, and 99.3%, respectively. Compared with other algorithms, the training time cost and recognition accuracy of the proposed algorithm are lower and higher, respectively; thus, the proposed algorithm is of great practical value.
Highlights
In the field of electronic counter-measures, only physical layer signals can be detected by sensors
The communication behavior signals used in the experiment were simulated according to MIL
An algorithm based on bispectral features and ameliorated LeNet was proposed in this study of short-wave radio station communication behavior recognition
Summary
In the field of electronic counter-measures, only physical layer signals can be detected by sensors. Research on the communication behavior of radio stations must be carried out by analyzing the physical layer signals. In the absence of communication protocol standards, as a non-collaborator, correctly recognizing communication behaviors has always been a difficult problem [1,2]. The communication behavior of a radio station represents the working state of the radio station, which helps us to infer the communication intention of the radio station’s holder. It is of great significance to carry out research on communication behaviors by directly using physical layer signals detected by sensors. Communication behaviors include “link establishment–link demolition”, “service request–service confirmation”, and “service transmission”
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