A novel feature extraction method for radar target classification using fusion of early-time and late-time regions

Seung-Jae Lee, In-Sik Choi and Dae-Young Chae

Journal of Electromagnetic Waves and Applications, 2017, vol. 31, issue 10, 1020-1033

Abstract: This paper proposes a feature vector fusion of early-time and late-time regions, which improves the performance of radar target classification. For verifying the performance of the proposed method, we use the calculated radar cross section (RCS) of four full-scale targets and measured the RCS of three scale model targets. Then, we extract a feature vector from a waveform structure in the early-time region. The resonance frequencies are extracted using an evolutionary programming (EP)-based CLEAN algorithm in the late-time region. The extracted feature vectors are passed through the feature fusion process and then used as inputs for a neural network classifier. The results show that the proposed method exhibits better performance than those that use either early-time or late-time features.

Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/09205071.2017.1324324 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tewaxx:v:31:y:2017:i:10:p:1020-1033

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tewa20

DOI: 10.1080/09205071.2017.1324324

Access Statistics for this article

Journal of Electromagnetic Waves and Applications is currently edited by Mohamad Abou El-Nasr and Pankaj Kumar Choudhury

More articles in Journal of Electromagnetic Waves and Applications from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().