Open AccessHRRP classification based on multi‐scale fusion sparsity preserving projections
Author(s) -
Dai Weilong,
Zhang Gong,
Zhang Yang
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2017.0684
Subject(s) - robustness (evolution) , pattern recognition (psychology) , feature extraction , artificial intelligence , computer science , fusion , support vector machine , scale (ratio) , feature (linguistics) , range (aeronautics) , data mining , engineering , biochemistry , chemistry , linguistics , philosophy , physics , quantum mechanics , gene , aerospace engineering
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.