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

Tao ZHANG; Juncheng GUO; Ran LAI

doi:10.11999/JEIT200114

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

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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

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

doi: 10.11999/JEIT200114 cstr: 32379.14.JEIT200114

Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China

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

Abstract

Abstract

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.
- Space-Time Adaptive Processing (STAP),
- Sparse recovery,
- Dictionary mismatch,
- Atomic norm

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References(23)

References

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