Abstract Applying sparse representation (SR) to a sample, the sparse vector can be obtained, which not only sufficiently describes the characteristic of the sample, but also has the capability of discriminant. A SAR target configuration recognition algorithm based on SR is proposed. Firstly, Feature extraction is implemented to eliminate the influence of the speckle noise. Then, a dictionary is constructed by all the training samples, and the testing sample is projected onto the dictionary to obtain the sparse vector. Finally, according to the fact that the difference between the samples with the same label and closest azimuth angles is the smallest, the principle of the minimum single sample reconstruction error is established to realize SAR target configuration recognition. The experiments on the MSTAR datasets validate the effectiveness of the proposed algorithm.
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2014, Vol. 27 
