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

• Published in: Multidimensional systems and signal processing Vol. 32; no. 4; pp. 1259 - 1276
• Main Authors: Zhang, Tao, Hu, Yongsheng, Lai, Ran
• Format: Journal Article
• Language: English
• 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
• ISSN: 0923-6082; 1573-0824
• DOI: 10.1007/s11045-021-00784-x

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.