Gridless super-resolution sparse recovery for non-sidelooking STAP using reweighted atomic norm minimization


The sparse recovery space–time adaptive processing (SR-STAP) can reduce the requirements of clutter samples and suppress clutter effectively using limited training samples for airborne radar. Commonly, the whole angle-Doppler plane is uniformly discretized into small grid points in SR-STAP methods. However, the clutter patches deviate from the pre-discretized grid points in a non-sidelooking SR-STAP radar. The off-grid effect degrades the SR-STAP performance significantly. In this paper, a gridless SR-STAP method based on reweighted atomic norm minimization is proposed, in which the clutter spectrum is precisely estimated in the continuous angle-Doppler domain without resolution limit. Numerical simulations are conducted and the results show that the proposed method can achieve better performance than the SR-STAP methods with discretized dictionaries and the SR-STAP methods utilizing atomic norm minimization.

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This work was supported in part by the National Natural Science Foundation of China and Civil Aviation Administration of China (Grant No. U1733116), Fundamental Research Funds for Central Universities-CAUC(3122019048), Young Scholar Foundation of Civil Aviation University of China

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Zhang, T., Hu, Y. & Lai, R. Gridless super-resolution sparse recovery for non-sidelooking STAP using reweighted atomic norm minimization. Multidim Syst Sign Process (2021).

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  • Airborne radar
  • Space–time adaptive processing
  • Off-grid
  • Reweighted atomic norm minimization