Sparse array radar forward-looking imaging based on fast atomic norm minimization. Issue 4 (3rd April 2023)

• Record Type: Journal Article
• Authors: Cao, Kaicheng; Cheng, Yongqiang; Liu, Qingping; Wang, Hongqiang
• Is Part Of: Remote sensing letters. Volume 14:Issue 4(2023)
• Page Start: 369
• Page End: 380
• Publication Date: 2023-04-03
• Subjects: sparse array radar -- forward-looking imaigng -- continues compressed sensing (CCS) -- fast atomic norm minimization (ANM); Remote sensing -- Periodicals; Remote sensing; Periodicals; 621.3678
• DOI: 10.1080/2150704X.2023.2203338
• Languages: English
• ISSNs: 2150-704X

Abstract

ABSTRACT: The sparse array imaging radar could largely reduce the system complexity, but it also suffers from poor azimuthal resolution due to the limited aperture length and incomplete observation caused by non-uniform array configuration. In this letter, a radar forward-looking imaging method with improved imaging quality and reduced system complexity is proposed. Firstly, the matrix completion method is utilized to recover the missing signals of sparse array. Then, based on the plane-wave assumption, a fast 2-dimensional atomic norm minimization (ANM) is adopted to solve the positions and amplitudes of scatterers. As a continues compressed sensing method, the result of ANM-based method earns sharper main-lobes and much lower-level side-lobes compared to the fast Fourier transform -based method. Although the resolution of proposed method is still limited by Rayleigh resolution, the imaging quality can be greatly improved by emphasizing main-lobes and avoiding the perturbation of side-lobes. Simulations and experiments are carried out to demonstrate the performance of the proposed method.