title: Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning.
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.
authors.affiliation: School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China. yangzhutian@hit.edu.cn.
authors.forename: Zhutian
authors.initials: Z
authors.lastname: Yang
authors.affiliation: State Key Laboratory of Urban Water Resources and Environment, Harbin Institute of Technology, Harbin 150001, China. qiuweihit@126.com.
authors.forename: Wei
authors.initials: W
authors.lastname: Qiu
authors.affiliation: School of Engineering and Computer Science, Durham University, Durham DH1 3LE, UK. hongjian.sun@durham.ac.uk.
authors.forename: Hongjian
authors.initials: H
authors.lastname: Sun
authors.affiliation: Department of Informatics, King's college London, London WC2R 2LS, UK. arumugam.nallanathan@kcl.ac.uk.
authors.forename: Arumugam
authors.initials: A
authors.lastname: Nallanathan
keywords: Wigner–Ville distribution; radar emitter recognition; relevance vector machine; three-dimensional distribution feature; transfer learning