Abstract:

A new approach to classify synthetic aperture radar (SAR) targets is presented based on high range resolution profile (HRRP) time-frequency non-negative sparse coding (NNSC). Firstly, complex SAR target images are converted into HRRPs. And the non-negative time-frequency matrix for each profile is obtained by using adaptive Gaussian representation (AGR). Secondly, NNSC is applied to learn target time-frequency dictionary. Feature vectors are constructed by projecting each HRR profile time-frequency matrix to the time-frequency dictionary. Finally, the target classification decision is found with the support vector machine. To demonstrate the performance of the proposed approach, experiments are performed with SAR database released publicly by moving and stationary target acquisition and recognition (MSTAR). The experiment results support the effectiveness of the proposed technique for SAR target classification.