Synthetic Aperture Rader Target Recognition Based on KPCA and Sparse Representation

SAR(Synthetic Aperture Rader) target recognition method based on KPCA and sparse representation is proposed. First, KPCA(Kernel Principal Component Analysis) feature extraction is used to get the feature of the samples. Then a sparse representation model is built in the feature space. The sparse coefficient is obtained by GPSR(Gradient Projection for Sparse Reconstruction). Finally, the recognition is achieved by computing the energy of the sparse coefficient. Experimental results with MSTAR(Moving and Stationary Target Acquisition and Recognition) SAR data sets show that the average recognition rate with the proposed method is up to 96.78% without knowing the target azimuth which can improve the target recognition result. And the proposed method is a effective method for SAR target recognition.