A specific emitter identification method based on full bispectrum and deep residual shrinkage network

Zihao Song; Wei Cheng; Jingwen Li; Xiaobai Li

doi:10.1117/12.2601017

30 June 2021 A specific emitter identification method based on full bispectrum and deep residual shrinkage network

Zihao Song, Wei Cheng, Jingwen Li, Xiaobai Li

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 1187826 (2021) https://doi.org/10.1117/12.2601017
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

To solve the problem of specific emitter identification (SEI) under the Rice channel, an SEI method based on full bispectrum and deep residual shrinkage networks (DRSN) is proposed. The method uses phase noise to model individual differences and utilizes non-parametric methods to extract the full bispectrum of the signal. Then the full bispectrum is used as the input of convolutional neural networks (CNN) and the model learns signal features and completes the classification task through multi-layer cascaded DRSN subsequently. The simulation experiment models 3 different radiation sources, and the modulation mode is set to BPSK, MSK, OQPSK. Experimental results show that the method we proposed can effectively identify different radiation sources, and also has a high recognition performance under low SNRs. Compared with other methods that use reduced-dimensional bispectrum features as input, the recognition accuracy is effectively improved.

Citation Download Citation

Zihao Song, Wei Cheng, Jingwen Li, and Xiaobai Li "A specific emitter identification method based on full bispectrum and deep residual shrinkage network", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 1187826 (30 June 2021); https://doi.org/10.1117/12.2601017

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