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Park C.-S., Choi J.-H., Nah S.-P., Jang W., Kim D.Y. 27–30 October 2017 Piscataway, NJ, USA: Institute of Electrical and Electronics Engineers (IEEE) 2017. Automatic digital modulation recognition based on stacked sparse autoencoder Proceedings of the 2017 IEEE 17th International Conference on Communication Technology (ICCT) Chengdu, China.
The experiments based on seven different radar emitter signals indicate that the proposed CNN-1D-AM has the advantages of high accuracy and superior performance in radar emitter signal recognition.Īttention mechanism one-dimensional convolutional neural network radar emitter signal recognition.īouchou M., Wang H., Lakhdari M.E.H. In this method, features of the given 1-D signal sequences are extracted directly by the 1-D convolutional layers and are weighted in accordance with their importance to recognition by the attention unit. In order to solve these problems, this paper proposes a novel one-dimensional convolutional neural network with an attention mechanism (CNN-1D-AM) to extract more discriminative features and recognize the radar emitter signals. Moreover, the features extracted from convolutional layers are redundant so that the recognition accuracy is low. However, in the field of radar emitter signal recognition, the data are usually one-dimensional (1-D), which takes more time and storage space than by using the original two-dimensional CNN model directly. In recent years, convolutional neural network (CNN) has shown its superiority in recognition so that experts have applied it in radar signal recognition. As the real electromagnetic environment grows complex and the quantity of radar signals turns massive, traditional methods, which require a large amount of prior knowledge, are time-consuming and ineffective for radar emitter signal recognition.
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