Specific emitter identification by wavelet residual network based on attention mechanism

Specific emitter identification technology plays a very important role in spectrum resource management, wireless network security, cognitive radio etc. However, in complex electromagnetic environments, the variability and uncertainty of signals make it very difficult to extract representative feature representations of the signals. At the same time, the feature extraction capability of the recognition model is also a factor that needs to be considered. To address these issues, a wavelet residual neural network model based on attention mechanism is proposed for specific emitter identification. First, multi-level wavelet decomposition is performed on all received signals to obtain their wavelet detail coefficients at different scales. Next, all the wavelet detail coefficients are used as the feature input for the attention-based residual network, and perform parallel feature extraction at multi scales. Finally, the feature representation capability of all coefficients are compared, and the model's recognition results based on it are obtained. The recognition rates on the three datasets are 94.7%, 93.21%, and 86.1%, respectively, all of which are superior to recent state-of-the-art algorithms. In addition, through ablation experiment, the validity of each component of the model has been verified.