%0 Electronic Article %A Long Li %A Zheng Liu %K dictionary learning %K denoising dictionary optimisation %K radar targets %K recognition performance %K hinge loss theory %K global characteristics %K discriminative dictionary %K noise-robust HRRP target recognition method %K noise suppression %K local characteristics %K discriminative sparse-low-rank representation %K relatively low SNR conditions %K training stage %K low signal-to-noise ratio conditions %K high-range resolution profiles %X A novel target recognition method is proposed for high-range resolution profiles (HRRPs) of radar targets under low signal-to-noise ratio (SNR) conditions. This method achieves good recognition performance for noisy HRRPs with discriminative sparse-low-rank representation. The framework of this method is constructed based on sparse representation and low-rank representation, which are applied to extract the local and global characteristics of target HRRPs. To guarantee the noise-robust and highly discriminative features of the HRRPs, dictionary learning is adopted. In the training stage, a discriminative dictionary is produced based on hinge loss theory to improve the recognition performance. Denoising dictionary optimisation is implemented for noise suppression during the testing stage. Experimental results on measured HRRP data demonstrate that the proposed method can recover the original HRRPs and significantly improve the recognition performance for HRRP test samples under relatively low SNR conditions. %@ 0013-5194 %T Noise-robust HRRP target recognition method via sparse-low-rank representation %B Electronics Letters %D November 2017 %V 53 %N 24 %P 1602-1604 %I Institution of Engineering and Technology %U https://digital-library.theiet.org/;jsessionid=3d1ilhghfqdnj.x-iet-live-01content/journals/10.1049/el.2017.2960 %G EN