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....

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Bibliographic Details
Published inMultidimensional systems and signal processing Vol. 32; no. 4; pp. 1259 - 1276
Main Authors Zhang, Tao, Hu, Yongsheng, Lai, Ran
Format Journal Article
LanguageEnglish
Published New York Springer US 01.10.2021
Springer Nature B.V
Subjects
Airborne radar
Artificial Intelligence
Circuits and Systems
Clutter
Discretization
Electrical Engineering
Engineering
Optimization
Recovery
Signal,Image and Speech Processing
Space-time adaptive processing
Reweighted atomic norm minimization
Airborne radar
Off-grid
Space–time adaptive processing
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Summary: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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ISSN:0923-6082
1573-0824
DOI:10.1007/s11045-021-00784-x