Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet

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.

Identification of Shortwave Radio Communication Behavior Based on Autocorrelation Spectrogram Features

“…Algorithms. Currently, the research on radio behavior recognition based on signals from the physical layer is in its infancy, and only the study in [33] carries out communication behavior recognition based on five types of burst waveforms. When the SNR is 15 dB, the recognition accuracy can reach 99.3%, but with the increase in noise, the recognition accuracy obviously decreases.…”

“…When the SNR is 0 dB, it is even lower than 50%. In this experiment, we compare the algorithm proposed in this paper with the algorithm in the literature [33] as well as the network model in this paper plus, the features in the literature [33], and the network model in this paper plus the features in the literature [33]. The experimental results are illustrated in Figure 15.…”

“…Research on communication behavior can be divided into communication group behavior [22,23] and communication individual behavior [24][25][26][27][28][29][30][31][32][33][34][35][36]. The former mainly refers to a series of combat tasks coordinated by groups equipped with CRS, while the latter mainly refers to the working modes, tasks of a certain CRS, and the actions of the person or unit operating the CRS.…”

“…[30,31]. The third method transforms the problem of communication behavior recognition into the problem of radio signal classification recognition by analyzing the behavior connotation of intercepted radio signals [32][33][34][35][36]. Liu et al proposed a method to discover the communication relationship of ultrashort-wave radio stations [24].…”

“…Zhou et al observed the differences in the deep-level features of shortwave burst waveforms and demonstrated the feasibility of directly identifying shortwave communication behaviors based on physical layer signals [32]. Wu et al used the improved LeNet to identify five communication behaviors [33]. Wu et al [34] used the improved onedimensional DenseNet to identify seven automatic link establishment (ALE) behaviors under the same types of burst waveform BW0.…”