A Fast Sparse Representation Method for SAR Target Configuration Recognition

Focusing on the problem of the real-time implementation in sparse representation (SR) based recognition algorithm, a fast sparse representation (FSR) algorithm is presented in this paper to improve the efficiency of synthetic aperture radar (SAR) target configuration recognition. Taking the inertia variance characteristic of SAR target images over a small range of azimuth angles into consideration, training samples of each configuration are averaged. Instead of using all the training samples to establish the dictionary in SR, the average samples are utilized to construct the dictionary in FSR. A small dictionary accelerates the speed of the proposed algorithm.