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The key problem in any decision-making system is to gather as much information as possible about the object or the phenomenon under study. In the case of the radar targets, frequency and angular information is integrated to form a radar image, which has very high information content. Salient features can then be extracted in order to characterize or to classify radar targets. The quality of the reconstructed image is mainly related to the resolution performed by the radar system both in slant range and in cross range. The Fourier-based reconstruction methods are fast and robust, but they are limited in resolution and dynamic range. Subspace eigenanalysis based methods, such as multiple signal classification (MUSIC) or estimation of signal parameters by rotational invariance techniques (ESPRIT), are able to provide superresolution and to accurately recover the scattering center locations even for a small number of correlated samples. The aim of the paper is to present some results of our ongoing research on the application of these techniques for radar imagery.