An Efficient K-SVD Algorithm of Dictionary Learning for HRRP Targets Recognition

DOI

10.2991/icmia-16.2016.92How to use a DOI?

Keywords

sparse classification, K-SVD, dictionary learning, high resolution range profile.

Abstract

Inspired by the characteristics of sparse representation, we consider to recognize three military targets. To overcome the target-aspect sensitivity in radar high resolution range profile (HRRP), an improved dictionary learning algorithm, called auto-optimized fast K-SVD (AOF-KSVD), is proposed in this paper. We introduce the correlation threshold and effectiveness threshold into the K-SVD first, and then use the fast batch orthogonal matching pursuit method to update the atoms in the dictionary, which not only reduced the computation complexity, also the time of dictionary learning. Finally, the experiments result validated the performance of the proposed method.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Kun Chen
AU  - Yuehua Li
AU  - Yilu Ma
PY  - 2016/11
DA  - 2016/11
TI  - An Efficient K-SVD Algorithm of Dictionary Learning for HRRP Targets Recognition
BT  - Proceedings of the 2016 5th International Conference on Measurement, Instrumentation and Automation (ICMIA 2016)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmia-16.2016.92
DO  - 10.2991/icmia-16.2016.92
ID  - Chen2016/11
ER  -