Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning

Yang, Zhutian; Qiu, Wei; Sun, Hongjian; Nallanathan, Arumugam

doi:10.3390/s16030289

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning

Yang, Zhutian; Qiu, Wei; Sun, Hongjian; Nallanathan, Arumugam

Authors

Zhutian Yang

Wei Qiu

Professor Hongjian Sun hongjian.sun@durham.ac.uk
Professor

Arumugam Nallanathan

Abstract

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches.

Citation

Yang, Z., Qiu, W., Sun, H., & Nallanathan, A. (2016). Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning. Sensors, 16(3), Article 289. https://doi.org/10.3390/s16030289

Journal Article Type	Article
Acceptance Date	Feb 9, 2016
Online Publication Date	Feb 25, 2016
Publication Date	Feb 25, 2016
Deposit Date	Mar 16, 2016
Publicly Available Date	Mar 17, 2016
Journal	Sensors
Publisher	MDPI
Peer Reviewed	Peer Reviewed
Volume	16
Issue	3
Article Number	289
DOI	https://doi.org/10.3390/s16030289

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This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.