Moving target indication using space-based early warning radar is important in military applications. For the space-based early warning radar, complicated non-stationary clutter characteristics are induced due to the high-speed movement of the radar platform and Earth's rotation, and more serious clutter non-homogeneity than the airborne radar scenario is caused by the large beam illumination region. Consequently, traditional Space-Time Adaptive Processing (STAP) methods, which have been widely

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... used in airborne early warning radar, cannot be applied directly to the space-based early warning radar. In this study, we analyze the characteristic of clutter distribution and build a novel STAP framework, where high-resolution clutter spectra used to construct the adaptive weights is estimated via a Convolutional Neural Network (CNN). First, clutter data sets were randomly simulated with different ranges of bin, latitude, spatial error, internal clutter motion, and coefficients of surface scattering, where the radar and satellite parameters were utilized as a priori knowledge. Then, we designed a two-dimensional CNN with five layers that converted a low-resolution clutter spectrum into a high-resolution spectrum. Finally, a space-time adaptive filter was calculated using the estimated high-resolution space-time spectrum and employed for clutter suppression and target detection. The simulation results show that the proposed CNN STAP can achieve sub-optimal performance under limited sample conditions, and a smaller computational load compared with a state-of-the-art sparse recovery STAP method. Therefore, this framework is suitable for practical application in space-based early warning radar.