Next Article in Journal
Fluorescence Characterization of Gold Modified Liposomes with Antisense N-myc DNA Bound to the Magnetisable Particles with Encapsulated Anticancer Drugs (Doxorubicin, Ellipticine and Etoposide)
Previous Article in Journal
Distributed Long-Gauge Optical Fiber Sensors Based Self-Sensing FRP Bar for Concrete Structure
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(3), 289; doi:10.3390/s16030289

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

1
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
2
State Key Laboratory of Urban Water Resources and Environment, Harbin Institute of Technology, Harbin 150001, China
3
School of Engineering and Computer Science, Durham University, Durham DH1 3LE, UK
4
Department of Informatics, King’s college London, London WC2R 2LS, UK
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 11 December 2015 / Revised: 5 February 2016 / Accepted: 19 February 2016 / Published: 25 February 2016
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [1034 KB, uploaded 25 February 2016]   |  

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. View Full-Text
Keywords: radar emitter recognition; Wigner–Ville distribution; three-dimensional distribution feature; transfer learning; relevance vector machine radar emitter recognition; Wigner–Ville distribution; three-dimensional distribution feature; transfer learning; relevance vector machine
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Yang, Z.; Qiu, W.; Sun, H.; Nallanathan, A. Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning. Sensors 2016, 16, 289.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top