A reweighted matrix completion algorithm for sparse inverse synthetic aperture radar imaging
نویسندگان
چکیده
Sparse inverse synthetic aperture radar (ISAR) imaging can be generally achieved by compressed sensing (CS) methods because sparse sampling disables the approach of conventional range Doppler. However, CS-based have to convert matrix into a vector when random is adopted, which results in huge memory usage and high computational complexity. Note that completion (MC) suitable for operations, recover data directly based on low-rank property echo matrix. In order improve performance MC approaches, novel reweighted method ISAR proposed this paper. To avoid using same singular value thresholding traditional achieve solution, scheme values applied restrain rank. Furthermore, weights current iteration are updated with their obtained former iteration, rather than fixed value. Then, alternating direction multipliers utilised alternatively optimise model, has an improved efficiency. Once full recovered, image via 2D fast Fourier transform. Experimental measured validate result improving within several seconds, tens times faster some reported low-rank-based methods.
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Inverse synthetic aperture radar imaging based on sparse signal processing
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1The school of ITEF, The University of Queensland, Brisbane 4072, Australia 2Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56122 Pisa, Italy 3 School of Information Technology & Electrical Engineering, University of Queensland, Brisbane 4072, Australia 4Radar Modelling & Analysis Group, Electronic Warfare & Radar Division, Defence Science & Technology Organisation...
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3 Introduction 4 Objectives 5 Review 5 Plans 8 References 9 Figures 10
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ژورنال
عنوان ژورنال: Iet Radar Sonar and Navigation
سال: 2022
ISSN: ['1751-8784', '1751-8792']
DOI: https://doi.org/10.1049/rsn2.12322