2012
DOI: 10.1016/j.sigpro.2011.06.011
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Robust ISAR imaging based on compressive sensing from noisy measurements

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Cited by 54 publications
(33 citation statements)
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“…Importantly, a new interpretation of the proposed algorithm is provided in the following. A novel reconstruction model deducted from Meridian prior is proposed [6] …”
Section: Novel Cs Isar Imaging Algorithmmentioning
confidence: 99%
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“…Importantly, a new interpretation of the proposed algorithm is provided in the following. A novel reconstruction model deducted from Meridian prior is proposed [6] …”
Section: Novel Cs Isar Imaging Algorithmmentioning
confidence: 99%
“…Consequently, sparsity enhancement evaluation via Lorentz curve and signal compressibility analysis in [6] also provide theoretical basis for log-sum minimization from another perspective. However, compared with the solution algorithm in [6], the proposed algorithm is much simpler and has lower computational cost.…”
Section: Novel Cs Isar Imaging Algorithmmentioning
confidence: 99%
See 1 more Smart Citation
“…And in [12], the algorithm is improved to overcome strong noise and clutter by combining coherent projectors and weighting with the CS optimization for ISAR image generation. In 2012, Zhao et al [14] proposed a novel reconstruction model called MCS model deduced from Meridian prior, and with the decrease of the number of pulses and signal-to-noise ratio MCS model exhibits better performance in terms of resolution and amplitude error than that of the Laplace-prior-based CS model.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, the CS-based short aperture and sparse aperture ISAR imaging methods have already attracted extensive attention and made preliminary progress [10][11][12][13][14][15][16][17]. However, all the above-mentioned work are under the assumption that the target's rotation during the CPI is uniform, so the echoed signal in a range cell is composed of single frequency components, and the corresponding sparse dictionary is a discrete Fourier matrix.…”
Section: Introductionmentioning
confidence: 99%