Title of Paper:HRRP classification based on multi-scale fusion sparsity preserving projections

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Affiliation of Author(s):电子信息工程学院

Journal:ELECTRONICS LETTERS

Key Words:RECOGNITION

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.

ISSN No.:0013-5194

Translation or Not:no

Date of Publication:2017-05-25

Co-author:Dai, Weilong,Zhang, Yang

Correspondence Author:ZHANG Gong