2014
DOI: 10.1109/tip.2014.2311735
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MIMO Radar 3D Imaging Based on Combined Amplitude and Total Variation Cost Function With Sequential Order One Negative Exponential Form

Abstract: In inverse synthetic aperture radar (ISAR) imaging, a target is usually regarded as consist of a few strong (specular) scatterers and the distribution of these strong scatterers is sparse in the imaging volume. In this paper, we propose to incorporate the sparse signal recovery method in 3D multiple-input multiple-output radar imaging algorithm. Sequential order one negative exponential (SOONE) function, which forms homotopy between 1 and 0 norms, is proposed to measure the sparsity. Gradient projection is use… Show more

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Cited by 53 publications
(41 citation statements)
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“…A more radical solution is a two-dimensional MIMO [116], preferably based on the compressive sensing approach [117]. These are exciting developments, but they are again prototypes in a laboratory and not suitable to be used in the field.…”
Section: Three-dimensional Imagingmentioning
confidence: 99%
“…A more radical solution is a two-dimensional MIMO [116], preferably based on the compressive sensing approach [117]. These are exciting developments, but they are again prototypes in a laboratory and not suitable to be used in the field.…”
Section: Three-dimensional Imagingmentioning
confidence: 99%
“…By using the sparse signal recovery algorithm, the imaging quality can be improved. This has been successfully shown in SAR imaging [7,8], ISAR imaging [9,10], MIMO radar imaging [11,12].…”
Section: Introductionmentioning
confidence: 93%
“…For a one dimensional sparse signal e, define an exponential function g σ (e) = e − |e| σ and a sparse pseudo norm G σ (e) = M − m g σ (e(m)) [12,13], where M is the length of e. It had been shown that when σ approaches +∞, G σ (e) approaches 1 norm with a ratio difference; when σ approaches 0, G σ (e) approaches 0 norm. Hence, when σ moves from +∞ to 0, G σ (e) moves smoothly from 1 norm to 0 norm.…”
Section: 0 Norms Homotopy Block Sparse Signal Recovery Algorithmmentioning
confidence: 99%
“…However, these two methods are computational expensive, especially of [9], where forming a 128 × 64 ISAR image needs over one hour using a prevalent personal computer. 1 0 norms homotopy based algorithm, by varying a parameter σ, builds a homotopy between 1 norm and 0 norm, and has superior performance [11,12]. Another merit is that 1 0 norms homotopy algorithm is easily extended to 2D and block sparse signal recovery cases and the computation speed is fast.…”
Section: Introductionmentioning
confidence: 99%