Radar Imaging based on Orthogonal Matching Pursuit via Sparse Constraint

This paper presents constraint generalized orthogonal matching pursuit (C-gOMP) approach to tackle the problem of clutter mitigation for radar imaging in the compressive sensing context. The generalized orthogonal matching pursuit algorithm (gOMP) in radar imaging process is susceptible to environme...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1237; no. 3; pp. 32006 - 32009
Main Authors Xia, ChaoYu, Yu, Jie, Gao, YuXiang, Tian, Xiang
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.06.2019
Subjects
Algorithms
Background noise
Clutter
Cost function
Greedy algorithms
Matched pursuit
Matching
Radar
Radar imaging
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Summary:This paper presents constraint generalized orthogonal matching pursuit (C-gOMP) approach to tackle the problem of clutter mitigation for radar imaging in the compressive sensing context. The generalized orthogonal matching pursuit algorithm (gOMP) in radar imaging process is susceptible to environmental noise interference. Therefore, a particular constraint condition is added to the recovery signal, and the noise component is restrained after the greedy iteration. The C-gOMP algorithm uses the cost function to impose more constraints on each coefficient to ensure the convergence of the whole function. To further analyze the performance of C-gOMP algorithm, the effectiveness of this algorithm is demonstrated by simulations. The results show that the proposed approach is very obvious at suppressing unwanted clutter and enhancing the desired targets, and C-gOMP algorithm brings an evident performance improvement and application prospect.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1237/3/032006