access icon free Radar pulse completion and high-resolution imaging with SAs based on reweighted ANM

© The Institution of Engineering and Technology
Received 22/07/2017, Accepted 20/03/2018, Revised 28/02/2018, Published 23/03/2018

In actual condition, array elements deficiency or transmission errors lead to incomplete data, which is called sparse aperture (SA) data. In inverse synthetic aperture radar (ISAR) imaging, this large-gaped data produces poor-quality ISAR images when using traditional range–Doppler algorithm. Recently, imaging algorithms based on compressed sensing (CS) theory alleviate this problem effectively because CS theory indicates that sparse signal can be reconstructed from incomplete measurements. However, the basis mismatch problem in CS-based algorithms may degrade the ISAR image. In this study, a reweighted atomic-norm minimisation (ANM) (RAM)-based imaging method is proposed. RAM is a gridless sparse method, which can enhance sparsity and resolution. RAM formulates an optimisation problem and iteratively carries out ANM with a sound reweighting strategy. By reformulating the RAM as a semi-definite programme, the echoes with full aperture (FA) are reconstructed from SA data. After that, ISAR imaging with the reconstructed FA data is achieved via the conventional azimuth compression method. Simulated and real data results demonstrate the effectiveness and superiority of the proposed method.

Inspec keywords: minimisation; iterative methods; radar resolution; mathematical programming; image reconstruction; image resolution; Doppler radar; compressed sensing; synthetic aperture radar; radar imaging

Other keywords: transmission error; gridless sparse method; optimisation problem; array element deficiency; reweighted ANM; sound reweighting strategy; high-resolution imaging; ISAR imaging; compressed sensing theory; radar pulse completion; reweighted atomic-norm minimisation; RAM-based imaging method; azimuth compression method; range-Doppler algorithm; semidefinite programming; SA data; full aperture data; inverse synthetic aperture radar imaging; FA data; sparse signal reconstruction; CS theory; sparse aperture data

Subjects: Optimisation techniques; Radar equipment, systems and applications; Optical, image and video signal processing; Interpolation and function approximation (numerical analysis)

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