Space-time adaptive processing (STAP) algorithms based on sparse recovery (SR) have been researched because of their low requirement training snapshots. However, once some portion clutter is not located the grids, i.e., off-grid problems, performances most SR-STAP degrade significantly. Reducing grid interval can mitigate effects, but brings strong column coherence dictionary, heavy computation...