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

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

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
• Electronic ISSN: 1424-8220
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 3
• Article Number: 289
• DOI: https://doi.org/10.3390/s16030289
• Public URL: https://durham-repository.worktribe.com/output/1389118