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Volume 43 Issue 5
May  2021
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Tao ZHANG, Juncheng GUO, Ran LAI. Gridless Sparse Recovery for Non-sidelooking Space-Time Adaptive Processing Based on Atomic Norm Minimization[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1235-1242. doi: 10.11999/JEIT200114
Citation: Tao ZHANG, Juncheng GUO, Ran LAI. Gridless Sparse Recovery for Non-sidelooking Space-Time Adaptive Processing Based on Atomic Norm Minimization[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1235-1242. doi: 10.11999/JEIT200114

Gridless Sparse Recovery for Non-sidelooking Space-Time Adaptive Processing Based on Atomic Norm Minimization

doi: 10.11999/JEIT200114
Funds:  The National Natural Science Foundation of China (U1733116), The Fundamental Research Foundation for Central Universities-CAUC(3122019048), The Young Scholar Foundation of Civil Aviation University of China
  • Received Date: 2020-02-21
  • Rev Recd Date: 2020-11-26
  • Available Online: 2020-12-01
  • Publish Date: 2021-05-18
  • Sparse recovery Space-Time Adaptive Processing (STAP) can reduce the requirements of clutter samples, and suppress effectively clutter using limited training samples for airborne radar. The whole space-time plane is discretized into small grid points uniformly in presently available sparse recovery STAP methods, however, the clutter ridge is not located exactly on the pre-discretized grid points in non-sidelooking STAP radar. The dictionary mismatch effect degrades the performance of STAP significantly. In this paper, a gridless sparse recovery STAP method is proposed based on Atomic Norm Minimization (ANM-STAP), which utilizes the low-rank property of the clutter covariance matrix. In the proposed method, the clutter spectrum is precisely estimated in continuous space-time plane without dictionary mismatch. Numerical results show that the proposed method provides an improved performance to the sparse recovery STAP methods with discretized dictionaries.
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