NDLI: HRRP classification based on multi-scale fusion sparsity preserving projections

Content Provider	IET Digital Library
Author	Dai, Weilong Zhang, Gong Zhang, Yang
Abstract	To improve the accuracy and robustness of high-resolution range profile (HRRP) target recognition, in this paper, the multi-scale fusion sparsity preserving projections (MSFSPP) approach is proposed for feature extraction. Compared with traditional multi-scale feature extraction method, the proposed MSFSPP approach utilises features in every scale and their sparse reconstructive relationship to construct multi-scale fusion features which contain more discriminating information. Support vector machine is employed to verify the classification performance of features extracted by MSFSPP and related feature extraction methods. Simulation results based on the measured aircraft datasets show that the proposed MSFSPP approach has outperformance with a small amount of data.
Starting Page	748
Ending Page	750
Page Count	3
ISSN	00135194
Volume Number	53
e-ISSN	1350911X
Issue Number	Issue 11, May (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/53/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2017.0684
Journal	Electronics Letters
Publisher Date	2017-04-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Aircraft Dataset Digital Signal Processing Electrical Engineering Computing Feature Extraction High-resolution Range Profile Target Recognition HRRP Classification HRRP Target Recognition Knowledge Engineering Technique MSFSPP Approach Multiscale Feature Extraction Method Multiscale Fusion Sparsity Preserving Projection Approach Object Recognition Radar Computing Radar Equipment Radar Resolution Radar Signal Processing Signal Classification Signal Processing And Detection Signal Reconstruction Support Vector Machine System And Application
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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