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

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 thr...

Full description

Saved in:
Bibliographic Details
Published in:Journal of electromagnetic waves and applications 2017-07, Vol.31 (10), p.1020-1033
Main Authors: Lee, Seung-Jae, Choi, In-Sik, Chae, Dae-Young
Format: Article
Language:English
Subjects:
complex natural resonance frequency
feature extraction
feature vector fusion
Radar target classification
waveform structure-based feature
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0920-5071
1569-3937
DOI:10.1080/09205071.2017.1324324