SAR Target Recognition Based on Modified Sparse Representation for Ground Safety

2019 International Conference on Advanced Electronic Materials, Computers and Materials Engineering

无线电电子学;计算机科学;材料科学

Ou, Wang^1 ; Wei, Li^1 ; Jieping, Han^2 ; Mingyu, Yang^1 ; Shanqi, Zheng^1

State Grid Liaoning Electric Power Supply Co. Ltd., Information and Telecommunication Branch, Shenyang

110006, China^1

Northeast Electric Power University, Changchun

132012, China^2

关键词: Hot topics;    Reconstruction error;    Sparse representation;    Sparse representation based classifications;    Target labels;    Target recognition;    Test samples;    Training class;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/563/4/042019/pdf
DOI  :  10.1088/1757-899X/563/4/042019

来源: IOP

【 摘 要 】

Recognition of synthetic aperture radar (SAR) targets is a hot topic in pattern recognition field. In the previous works, the sparse representation-based classification (SRC) is successfully used in SAR target recognition with high performance. The traditional SRC is performed on the global dictionary from the training classes. As a result, the representation capability of an individual class is not fully considered. This paper modifies the traditional SRC by performing the sparse representation over the local dictionaries formed by individual classes. In this way, the reconstruction error from one class can better reflect its representation capability as for describing the test sample. By comparing the reconstruction errors of different training classes, the target label of test sample can be classified finally. In the experiments, the MSTAR dataset is used to test the proposed method, which show the good results of the proposed method.